Role of BRC in arid rock weathering
Data analysis and plotting for publication
Roey Angel (roey.angel@bc.cas.cz)
2019-01-04
New insights into the role of epilithic biological crust in arid rock weathering
This script reproduces all sequence analysis steps and plots included in the paper plus some additional exploratory analyses. The analysis is heavily based on the phyloseq package (McMurdie and Holmes 22AD–2013), but also on many other R packages.
set.seed(123456789)
bootstraps <- 1000
min_lib_size <- 1000Load data
read.csv("Data/Rock_weathering_new2_otuTab.txt", header = TRUE, row.names = 1, sep = "\t") %>%
t() %>%
as.data.frame() ->
Rock_weathering_OTUmat
sort_order <- as.numeric(gsub("OTU([0-9]+)", "\\1", colnames(Rock_weathering_OTUmat)))
Rock_weathering_OTUmat <- Rock_weathering_OTUmat[, order(sort_order)]
row.names(Rock_weathering_OTUmat) <- gsub("(.*)Nimrod[0-9]+|Osnat[0-9]+", "\\1", row.names(Rock_weathering_OTUmat))
Metadata <- read.csv("Data/Rock_weathering_metadata_RA.csv", row.names = 1, header = TRUE)
# Order abundance_mat samples according to the metadata
sample_order <- match(row.names(Rock_weathering_OTUmat), row.names(Metadata))
Rock_weathering_OTUmat %<>% arrange(., sample_order)
Metadata$sample_names <- row.names(Metadata)
Metadata$Uni.Source <- fct_collapse(Metadata$Source, Rock = c("Dolomite", "Limestone"))
Metadata$Climate.Source <-
factor(
paste(
Metadata$Climate,
Metadata$Source
),
levels = c(
"Arid Limestone",
"Arid Dust",
"Arid Loess soil",
"Hyperarid Dolomite",
"Hyperarid Dust",
"Hyperarid Loess soil"
),
labels = c(
"Arid limestone",
"Arid dust",
"Arid loess soil",
"Hyperarid dolomite",
"Hyperarid dust",
"Hyperarid loess soil"
)
)
Metadata$Climate.UniSource <-
factor(
paste(
Metadata$Climate,
Metadata$Uni.Source
),
levels = c(
"Arid Rock",
"Arid Dust",
"Arid Loess soil",
"Hyperarid Rock",
"Hyperarid Dust",
"Hyperarid Loess soil"
),
labels = c(
"Arid rock",
"Arid dust",
"Arid loess soil",
"Hyperarid rock",
"Hyperarid dust",
"Hyperarid loess soil"
)
)
# calculate sample size
Metadata$Lib.size = rowSums(Rock_weathering_OTUmat)
row.names(Rock_weathering_OTUmat) <- row.names(Metadata)
# Load taxonomy data
tax.file <- "Data/Rock_weathering_new2_silva.nrv119.taxonomy"
Taxonomy <- read.table(tax.file, stringsAsFactors = FALSE) # read taxonomy file
# count how many ';' in each cell and add up to 6
for (i in 1:nrow(Taxonomy)) {
semicolons <- length(gregexpr(";", Taxonomy$V2[i])[[1]])
if (semicolons < 6) {
x <- paste0(rep("Unclassified;", 6 - semicolons), collapse = "")
Taxonomy$V2[i] <- paste0(Taxonomy$V2[i], x, sep = "")
}
}
do.call( "rbind", strsplit( Taxonomy$V1, ";", fixed = TRUE)) %>%
gsub( "size=([0-9]+)", "\\1", .) %>%
data.frame( ., do.call( "rbind", strsplit( Taxonomy$V2, ";", fixed = TRUE)), stringsAsFactors = F) %>%
apply(., 2, function(x) gsub( "\\(.*\\)", "", x)) %>%
replace(., . == "unclassified", "Unclassified") ->
Taxonomy
colnames( Taxonomy ) <- c( "OTU", "Frequency", "Domain", "Phylum", "Class", "Order", "Family", "Genus" )
# rownames(Taxonomy) <- colnames(Rock_weathering_OTUmat)
rownames(Taxonomy) <- Taxonomy[, 1]
# generate phyloseq object
Rock_dust <- phyloseq(otu_table(Rock_weathering_OTUmat, taxa_are_rows = FALSE),
tax_table(Taxonomy[, -c(1, 2)]),
sample_data(Metadata)
)
# Reorder factors for plotting
sample_data(Rock_dust)$Source %<>% fct_relevel("Limestone", "Dolomite", "Dust", "Loess soil")Remove samples not for analysis
samples2remove <- c(2, 3, 4, 5, 6, 7, 8, 10, 12)
Rock_dust <- subset_samples(Rock_dust, !grepl(paste(c(sample_names(Rock_dust)[samples2remove]), collapse = "|"), sample_names(Rock_dust)))
Rock_dust <- filter_taxa(Rock_dust, function(x) sum(x) > 0, TRUE)
domains2remove <- c("", "Eukaryota", "Unclassified")
classes2remove <- c("Chloroplast")
families2remove <- c("Mitochondria")
Rock_weathering_filt <- subset_taxa(Rock_dust, !is.na(Phylum) &
!Domain %in% domains2remove &
!Class %in% classes2remove &
!Family %in% families2remove)First let’s explore the prevalence of different taxa in the database.
prevdf <- apply(X = otu_table(Rock_weathering_filt),
MARGIN = ifelse(taxa_are_rows(Rock_weathering_filt), yes = 1, no = 2),
FUN = function(x){sum(x > 0)})
# Add taxonomy and total read counts to this data.frame
prevdf <- data.frame(Prevalence = prevdf,
TotalAbundance = taxa_sums(Rock_weathering_filt),
tax_table(Rock_weathering_filt))
prevdf %>%
group_by(Phylum) %>%
summarise(`Mean prevalence` = mean(Prevalence),
`Sum prevalence` = sum(Prevalence)) ->
Prevalence_phylum_summary
Prevalence_phylum_summary %>%
kable(., digits = c(0, 1, 0)) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = F)| Phylum | Mean prevalence | Sum prevalence |
|---|---|---|
| Acidobacteria | 16.9 | 1281 |
| Actinobacteria | 20.1 | 5660 |
| Aquificae | 14.3 | 43 |
| Armatimonadetes | 15.8 | 79 |
| Bacteroidetes | 16.2 | 2337 |
| Caldiserica | 2.0 | 2 |
| Candidate_division_BRC1 | 14.0 | 28 |
| Candidate_division_OD1 | 20.2 | 81 |
| Candidate_division_OP11 | 9.0 | 9 |
| Candidate_division_TM7 | 18.3 | 440 |
| Chlorobi | 15.5 | 62 |
| Chloroflexi | 14.9 | 2753 |
| Cyanobacteria | 19.4 | 874 |
| Deinococcus-Thermus | 19.6 | 274 |
| Elusimicrobia | 11.0 | 22 |
| Fibrobacteres | 16.2 | 65 |
| Firmicutes | 16.2 | 1164 |
| Fusobacteria | 14.0 | 28 |
| Gemmatimonadetes | 15.4 | 848 |
| Nitrospirae | 22.0 | 44 |
| NPL-UPA2 | 6.0 | 6 |
| Planctomycetes | 12.7 | 420 |
| Proteobacteria | 19.1 | 4979 |
| SBYG-2791 | 9.0 | 9 |
| SM2F11 | 20.0 | 20 |
| Spirochaetae | 15.0 | 15 |
| Synergistetes | 11.0 | 11 |
| Tenericutes | 3.0 | 3 |
| Thaumarchaeota | 21.0 | 21 |
| Verrucomicrobia | 13.2 | 383 |
| WCHB1-60 | 19.0 | 57 |
prevdf %>%
group_by(Order) %>%
summarise(`Mean prevalence` = mean(Prevalence),
`Sum prevalence` = sum(Prevalence)) ->
Prevalence_Order_summary
Prevalence_Order_summary %>%
kable(., digits = c(0, 1, 0)) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = F)| Order | Mean prevalence | Sum prevalence |
|---|---|---|
| 11B-2 | 9.2 | 55 |
| Acidimicrobiales | 18.2 | 709 |
| Acidithiobacillales | 21.0 | 21 |
| Actinomycetales | 12.0 | 12 |
| Aeromonadales | 21.0 | 21 |
| AKIW781 | 11.0 | 592 |
| AKYG1722 | 17.4 | 157 |
| Alteromonadales | 14.5 | 29 |
| Anaerolineales | 21.0 | 63 |
| Aquificales | 14.3 | 43 |
| Ardenticatenales | 9.8 | 98 |
| AT425-EubC11_terrestrial_group | 18.2 | 328 |
| Bacillales | 18.3 | 475 |
| Bacteroidales | 10.7 | 192 |
| BD2-11_terrestrial_group | 26.0 | 26 |
| BD72BR169 | 14.0 | 14 |
| Bdellovibrionales | 14.5 | 58 |
| BG.g7 | 26.0 | 26 |
| Bifidobacteriales | 9.0 | 18 |
| Brocadiales | 15.5 | 31 |
| Burkholderiales | 19.3 | 559 |
| C0119 | 12.0 | 120 |
| Caenarcaniphilales | 23.0 | 23 |
| Caldilineales | 17.0 | 102 |
| Caldisericales | 2.0 | 2 |
| Campylobacterales | 14.6 | 73 |
| Caulobacterales | 23.4 | 257 |
| Chlorobiales | 15.5 | 62 |
| Chloroflexales | 7.4 | 52 |
| Chromatiales | 16.8 | 84 |
| Chthoniobacterales | 14.1 | 226 |
| Clostridiales | 15.4 | 401 |
| Corynebacteriales | 16.9 | 203 |
| Cytophagales | 18.6 | 1113 |
| Dehalococcoidales | 8.0 | 16 |
| Deinococcales | 19.6 | 255 |
| Desulfobacterales | 8.4 | 42 |
| Desulfovibrionales | 8.0 | 8 |
| Desulfurellales | 11.0 | 11 |
| Desulfuromonadales | 22.0 | 22 |
| Elev-16S-976 | 23.5 | 47 |
| EMP-G18 | 3.0 | 3 |
| Enterobacteriales | 27.0 | 135 |
| Erysipelotrichales | 11.0 | 33 |
| Euzebyales | 19.4 | 252 |
| Fibrobacterales | 16.2 | 65 |
| Flavobacteriales | 16.4 | 164 |
| Frankiales | 28.2 | 367 |
| Fusobacteriales | 14.0 | 28 |
| Gaiellales | 18.4 | 386 |
| Gammaproteobacteria_Incertae_Sedis | 8.0 | 8 |
| Gemmatimonadales | 14.4 | 446 |
| GR-WP33-30 | 23.0 | 46 |
| HOC36 | 8.0 | 8 |
| JG30-KF-CM45 | 20.8 | 604 |
| Kineosporiales | 24.2 | 145 |
| Lactobacillales | 14.4 | 158 |
| Legionellales | 9.0 | 27 |
| Lineage_IIb | 9.0 | 9 |
| Lineage_IV | 13.0 | 13 |
| LNR_A2-18 | 15.0 | 15 |
| Methylophilales | 11.5 | 23 |
| Micrococcales | 24.3 | 365 |
| Micromonosporales | 16.2 | 130 |
| Myxococcales | 15.4 | 200 |
| Neisseriales | 14.5 | 58 |
| Nitriliruptorales | 21.3 | 64 |
| Nitrosomonadales | 28.0 | 28 |
| Nitrospirales | 22.0 | 44 |
| NKB5 | 21.0 | 21 |
| Obscuribacterales | 12.5 | 50 |
| Oceanospirillales | 15.0 | 15 |
| Opitutales | 10.0 | 30 |
| Order_II | 16.0 | 32 |
| Order_III | 16.0 | 48 |
| Order_IV | 26.0 | 26 |
| Pasteurellales | 14.0 | 14 |
| Phycisphaerales | 19.0 | 19 |
| Planctomycetales | 12.3 | 344 |
| Propionibacteriales | 19.6 | 352 |
| Pseudomonadales | 19.6 | 372 |
| Pseudonocardiales | 20.7 | 352 |
| PYR10d3 | 22.0 | 22 |
| Rhizobiales | 20.8 | 955 |
| Rhodobacterales | 21.9 | 219 |
| Rhodocyclales | 19.0 | 57 |
| Rhodospirillales | 18.0 | 450 |
| Rickettsiales | 19.2 | 115 |
| Rubrobacterales | 28.5 | 484 |
| S0134_terrestrial_group | 9.6 | 48 |
| SC-I-84 | 15.0 | 15 |
| Selenomonadales | 19.0 | 19 |
| Solirubrobacterales | 21.0 | 989 |
| Sphaerobacterales | 14.3 | 43 |
| Sphingobacteriales | 15.3 | 751 |
| Sphingomonadales | 24.0 | 552 |
| Streptomycetales | 23.0 | 69 |
| Streptosporangiales | 22.5 | 45 |
| Subgroup_10 | 15.0 | 30 |
| Subgroup_2 | 13.0 | 13 |
| Subgroup_3 | 16.1 | 129 |
| Subgroup_4 | 16.9 | 523 |
| Subgroup_6 | 19.2 | 441 |
| Subgroup_7 | 18.0 | 90 |
| SubsectionI | 20.7 | 62 |
| SubsectionII | 23.8 | 285 |
| SubsectionIII | 18.5 | 351 |
| SubsectionIV | 19.0 | 76 |
| Synergistales | 11.0 | 11 |
| Thermales | 19.0 | 19 |
| Thermoanaerobacterales | 13.2 | 53 |
| Thermogemmatisporales | 14.5 | 29 |
| Thermophilales | 22.8 | 91 |
| Thiotrichales | 13.0 | 39 |
| TRA3-20 | 20.2 | 101 |
| Unclassified | 16.7 | 2355 |
| Unknown_Order | 14.6 | 73 |
| Vampirovibrionales | 12.0 | 12 |
| Verrucomicrobiales | 8.0 | 24 |
| Xanthomonadales | 17.9 | 233 |
Based on that we’ll remove all phyla with a prevalence of under 7
Prevalence_phylum_summary %>%
filter(`Sum prevalence` < 7) %>%
select(Phylum) %>%
map(as.character) %>%
unlist() ->
filterPhyla
Rock_weathering_filt2 <- subset_taxa(Rock_weathering_filt, !Phylum %in% filterPhyla)
sample_data(Rock_weathering_filt2)$Lib.size <- rowSums(otu_table(Rock_weathering_filt2))
print(Rock_weathering_filt)## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1259 taxa and 34 samples ]
## sample_data() Sample Data: [ 34 samples by 13 sample variables ]
## tax_table() Taxonomy Table: [ 1259 taxa by 6 taxonomic ranks ]print(Rock_weathering_filt2)## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1256 taxa and 34 samples ]
## sample_data() Sample Data: [ 34 samples by 13 sample variables ]
## tax_table() Taxonomy Table: [ 1256 taxa by 6 taxonomic ranks ]Plot general prevalence features of the phyla
# Subset to the remaining phyla
prevdf_phylum_filt <- subset(prevdf, Phylum %in% get_taxa_unique(Rock_weathering_filt2, "Phylum"))
ggplot(prevdf_phylum_filt,
aes(TotalAbundance, Prevalence / nsamples(Rock_weathering_filt2), color = Phylum)) +
# Include a guess for parameter
geom_hline(yintercept = 0.05,
alpha = 0.5,
linetype = 2) + geom_point(size = 2, alpha = 0.7) +
scale_x_log10() + xlab("Total Abundance") + ylab("Prevalence [Frac. Samples]") +
facet_wrap( ~ Phylum) + theme(legend.position = "none")
Plot general prevalence features of the top 20 orders
# Subset to the remaining phyla
prevdf_order_filt <- subset(prevdf, Order %in% get_taxa_unique(Rock_weathering_filt2, "Order"))
# grab the top 30 most abundant orders
prevdf_order_filt %>%
group_by(Order) %>%
summarise(Combined.abundance = sum(TotalAbundance)) %>%
arrange(desc(Combined.abundance)) %>%
.[1:30, "Order"] ->
Orders2plot
prevdf_order_filt2 <- subset(prevdf, Order %in% Orders2plot$Order)
ggplot(prevdf_order_filt2,
aes(TotalAbundance, Prevalence / nsamples(Rock_weathering_filt2), color = Order)) +
# Include a guess for parameter
geom_hline(yintercept = 0.05,
alpha = 0.5,
linetype = 2) + geom_point(size = 2, alpha = 0.7) +
scale_x_log10() + xlab("Total Abundance") + ylab("Prevalence [Frac. Samples]") +
facet_wrap( ~ Order) + theme(legend.position = "none")
Unsupervised filtering by prevalence
We’ll remove all sequences which appear in less than 10% of the samples
# Define prevalence threshold as 10% of total samples
prevalenceThreshold <- 0.1 * nsamples(Rock_weathering_filt)
prevalenceThreshold## [1] 3.4# Execute prevalence filter, using `prune_taxa()` function
keepTaxa <-
row.names(prevdf_phylum_filt)[(prevdf_phylum_filt$Prevalence >= prevalenceThreshold)]
Rock_weathering_filt3 <- prune_taxa(keepTaxa, Rock_weathering_filt2)
sample_data(Rock_weathering_filt3)$Lib.size <- rowSums(otu_table(Rock_weathering_filt3))
print(Rock_weathering_filt2)## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1256 taxa and 34 samples ]
## sample_data() Sample Data: [ 34 samples by 13 sample variables ]
## tax_table() Taxonomy Table: [ 1256 taxa by 6 taxonomic ranks ]print(Rock_weathering_filt3)## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1249 taxa and 34 samples ]
## sample_data() Sample Data: [ 34 samples by 13 sample variables ]
## tax_table() Taxonomy Table: [ 1249 taxa by 6 taxonomic ranks ]This removed 7 or 0.557% of the sequences.
Exploring Rock_dust dataset features
First let’s look at the count data distribution
PlotLibDist(Rock_weathering_filt3)
sample_data(Rock_weathering_filt3) %>%
remove_rownames %>%
select(sample_title, Lib.size) %>%
as(., "data.frame") %>%
kable(.) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = F)| sample_title | Lib.size |
|---|---|
| Arid_Settled Dust_1 | 1562 |
| Hyperarid_Loess soil_8 | 6536 |
| Hyperarid_Loess soil_10 | 3921 |
| Hyperarid_Loess soil_12 | 4935 |
| Arid_Settled Dust_2 | 4421 |
| Hyperarid_Settled Dust_1 | 1001 |
| Hyperarid_Settled Dust_2 | 16095 |
| Arid_Limestone_1 | 9765 |
| Arid_Limestone_2 | 9130 |
| Arid_Limestone_3 | 11218 |
| Arid_Limestone_4 | 13838 |
| Arid_Limestone_5 | 11177 |
| Arid_Limestone_6 | 10781 |
| Arid_Limestone_7 | 15417 |
| Arid_Limestone_8 | 9721 |
| Arid_Limestone_9 | 20927 |
| Arid_Limestone_10 | 16812 |
| Arid_Limestone_11 | 14325 |
| Arid_Limestone_12 | 5112 |
| Hyperarid_Dolomite_1 | 62166 |
| Hyperarid_Dolomite_2 | 73930 |
| Hyperarid_Dolomite_3 | 123438 |
| Hyperarid_Dolomite_4 | 74161 |
| Hyperarid_Dolomite_5 | 98998 |
| Hyperarid_Dolomite_6 | 97834 |
| Hyperarid_Dolomite_7 | 160207 |
| Hyperarid_Dolomite_8 | 78535 |
| Hyperarid_Dolomite_9 | 47155 |
| Hyperarid_Dolomite_10 | 52276 |
| Hyperarid_Dolomite_11 | 63267 |
| Hyperarid_Dolomite_12 | 53859 |
| Arid_Loess soil_1 | 61130 |
| Arid_Loess soil_2 | 62204 |
| Arid_Loess soil_3 | 55724 |
The figure and table indicate only a small deviation in the number of reads per samples.
(mod1 <- adonis(
otu_table(Rock_weathering_filt3) ~ Lib.size,
data = as(sample_data(Rock_weathering_filt3), "data.frame"),
method = "bray",
permutations = 9999
))##
## Call:
## adonis(formula = otu_table(Rock_weathering_filt3) ~ Lib.size, data = as(sample_data(Rock_weathering_filt3), "data.frame"), permutations = 9999, method = "bray")
##
## Permutation: free
## Number of permutations: 9999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Lib.size 1 2.5245 2.52447 8.7483 0.21469 1e-04 ***
## Residuals 32 9.2341 0.28857 0.78531
## Total 33 11.7586 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1PlotReadHist(as(otu_table(Rock_weathering_filt3), "matrix"))
notAllZero <- (rowSums(t(otu_table(Rock_weathering_filt3))) > 0)
meanSdPlot(as.matrix(log2(t(otu_table(Rock_weathering_filt3))[notAllZero, ] + 1)))
Account for variation in library read-depth
We’ll use the GMPR method (J. Chen and Chen 2017)
Rock_weathering_filt3_GMPR <- Rock_weathering_filt3
Rock_weathering_filt3 %>%
otu_table(.) %>%
t() %>%
as(., "matrix") %>%
GMPR() ->
GMPR_factors## Begin GMPR size factor calculation ...
## Completed!
## Please watch for the samples with limited sharing with other samples based on NSS! They may be outliers!Rock_weathering_filt3 %>%
otu_table(.) %>%
t() %*% diag(1 / GMPR_factors$gmpr) %>%
t() %>%
as.data.frame(., row.names = sample_names(Rock_weathering_filt3)) %>%
otu_table(., taxa_are_rows = FALSE) ->
otu_table(Rock_weathering_filt3_GMPR)
sample_data(Rock_weathering_filt3_GMPR)$Lib.size <- sample_sums(Rock_weathering_filt3_GMPR)
adonis(
otu_table(Rock_weathering_filt3_GMPR) ~ Lib.size,
data = as(sample_data(Rock_weathering_filt3_GMPR), "data.frame"),
method = "bray",
permutations = 9999
)##
## Call:
## adonis(formula = otu_table(Rock_weathering_filt3_GMPR) ~ Lib.size, data = as(sample_data(Rock_weathering_filt3_GMPR), "data.frame"), permutations = 9999, method = "bray")
##
## Permutation: free
## Number of permutations: 9999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Lib.size 1 2.5834 2.58337 9.4557 0.22809 1e-04 ***
## Residuals 32 8.7426 0.27321 0.77191
## Total 33 11.3260 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1PlotLibDist(Rock_weathering_filt3_GMPR)
PlotReadHist(as(otu_table(Rock_weathering_filt3_GMPR), "matrix"))
notAllZero <- (rowSums(t(otu_table(Rock_weathering_filt3_GMPR))) > 0)
meanSdPlot(as.matrix(log2(t(otu_table(Rock_weathering_filt3_GMPR))[notAllZero, ] + 1)))
Alpha diversity
Calculate and plot alpha diversity mertrics.
# non-parametric richness estimates
rarefaction.mat <- matrix(0, nrow = nsamples(Rock_weathering_filt3), ncol = bootstraps)
rownames(rarefaction.mat) <- sample_names(Rock_weathering_filt3)
rich.ests <- list(S.obs = rarefaction.mat, S.chao1 = rarefaction.mat, se.chao1 = rarefaction.mat,
S.ACE = rarefaction.mat, se.ACE = rarefaction.mat)
for (i in seq(bootstraps)) {
sub.OTUmat <- rrarefy(otu_table(Rock_weathering_filt3), min(rowSums(otu_table(Rock_weathering_filt3))))
for (j in seq(length(rich.ests))) {
rich.ests[[j]][, i] <- t(estimateR(sub.OTUmat))[, j]
}
}
Richness <- data.frame(row.names = row.names(rich.ests[[1]]))
for (i in c(1, seq(2, length(rich.ests), 2))) {
S <- apply(rich.ests[[i]], 1, mean)
if (i == 1) {
se <- apply(rich.ests[[i]], 1, function(x) (mean(x)/sqrt(length(x))))
} else se <- apply(rich.ests[[i + 1]], 1, mean)
Richness <- cbind(Richness, S, se)
}
colnames(Richness) <- c("S.obs", "S.obs.se", "S.chao1", "S.chao1.se", "S.ACE", "S.ACE.se")
saveRDS(Richness, file = "Results/Rock_weathering_Richness.Rds")
write.csv(Richness, file = "Results/Rock_weathering_Richness.csv")
ses <- grep("\\.se", colnames(Richness))
Richness[, ses] %>%
gather(key = "est.se") -> se.dat
Richness[, -unique(ses)] %>%
gather(key = "est") -> mean.dat
n <- length(unique(mean.dat$est))
# diversity indices
diversity.inds <- list(Shannon = rarefaction.mat, inv.simpson = rarefaction.mat, BP = rarefaction.mat)
for (i in seq(bootstraps)) {
sub.OTUmat <- rrarefy(otu_table(Rock_weathering_filt3), min(rowSums(otu_table(Rock_weathering_filt3))))
diversity.inds$Shannon[, i] <- diversityresult(sub.OTUmat, index = 'Shannon', method = 'each site', digits = 3)[, 1]
diversity.inds$inv.simpson[, i] <- diversityresult(sub.OTUmat, index = 'inverseSimpson', method = 'each site', digits = 3)[, 1]
diversity.inds$BP[, i] <- diversityresult(sub.OTUmat, index = 'Berger', method = 'each site', digits = 3)[, 1]
}
Diversity <- data.frame(row.names = row.names(diversity.inds[[1]]))
for (i in seq(length(diversity.inds))) {
S <- apply(diversity.inds[[i]], 1, mean)
se <- apply(diversity.inds[[i]], 1, function(x) (mean(x)/sqrt(length(x))))
Diversity <- cbind(Diversity, S, se)
}
colnames(Diversity) <- c("Shannon", "Shannon.se", "Inv.simpson", "Inv.simpson.se", "BP", "BP.se")
ses <- grep("\\.se", colnames(Diversity))
Diversity[, ses] %>% gather(key = "est.se") -> se.dat
Diversity[, -unique(ses)] %>% gather(key = "est") -> mean.dat
saveRDS(Diversity, file = "Results/Rock_weathering_Diversity.Rds")
write.csv(Diversity, file = "Results/Rock_weathering_Diversity.csv")Test the differences in alpha diversity.
Plot all alpha diversity metrics together
Richness_Diversity_long[Richness_Diversity_long$Metric != "Chao1" &
Richness_Diversity_long$Metric != "Inv. Simpson" &
Richness_Diversity_long$Metric != "Berger Parker", ] %>%
droplevels() ->
Richness_Diversity_long2plot
p_alpha <- ggplot(Richness_Diversity_long2plot, aes(
x = Source,
y = Estimate
)) +
geom_violin(aes(colour = Climate, fill = Climate), alpha = 1/3) +
geom_jitter(aes(colour = Climate, fill = Climate), shape = 16, size = 2, width = 0.2, alpha = 2/3) +
scale_colour_manual(values = pom4, name = "") +
scale_fill_manual(values = pom4, name = "") +
theme_cowplot(font_size = 11, font_family = f_name) +
# geom_errorbar(alpha = 1 / 2, width = 0.3) +
xlab("") +
ylab("") +
theme(axis.text.x = element_text(
angle = 45,
vjust = 0.9,
hjust = 0.9
)) +
facet_grid(Metric ~ Climate, shrink = FALSE, scale = "free") +
background_grid(major = "y",
minor = "none") +
theme(panel.spacing = unit(2, "lines"))
dat_text <- data.frame(
label = as.character(fct_c(ph_Sobs$groups$groups, ph_ACE$groups$groups, ph_Shannon$groups$groups)),
Metric = rep(levels(Richness_Diversity_long2plot$Metric), each = 6),
Climate = str_split(rownames(ph_Sobs$groups), ":", simplify = TRUE)[, 1],
x = c("Loess soil", "Loess soil", "Limestone", "Dust", "Dolomite", "Dust"),
# x = as.factor(levels(Richness_Diversity_long2plot$Climate.Source)),
y = rep(c(460, 850, 6.5), each = 6)
# y = rep(c(40, 140, 0.5), each = 6)
)
p_alpha <- p_alpha + geom_text(
data = dat_text,
mapping = aes(x = x, y = y, label = label),
nudge_x = -0.2,
nudge_y = -0.1
)
print(p_alpha)
Richness_Diversity_long2plot %>%
group_by(Metric, Climate.Source) %>% # the grouping variable
summarise(mean_PL = mean(Estimate), # calculates the mean of each group
sd_PL = sd(Estimate), # calculates the standard deviation of each group
n_PL = n(), # calculates the sample size per group
SE_PL = sd(Estimate)/sqrt(n())) %>% # calculates the standard error of each group
kable(.) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = F)| Metric | Climate.Source | mean_PL | sd_PL | n_PL | SE_PL |
|---|---|---|---|---|---|
| S obs. | Arid limestone | 181.614750 | 51.1677757 | 12 | 14.7708645 |
| S obs. | Arid dust | 169.249500 | 109.0153596 | 2 | 77.0855000 |
| S obs. | Arid loess soil | 416.015667 | 8.2772347 | 3 | 4.7788637 |
| S obs. | Hyperarid dolomite | 128.760500 | 31.2564362 | 12 | 9.0229559 |
| S obs. | Hyperarid dust | 107.261000 | 88.7263447 | 2 | 62.7390000 |
| S obs. | Hyperarid loess soil | 220.405000 | 54.3993291 | 3 | 31.4074673 |
| ACE | Arid limestone | 353.781788 | 68.8104346 | 12 | 19.8638615 |
| ACE | Arid dust | 334.651440 | 143.6013458 | 2 | 101.5414854 |
| ACE | Arid loess soil | 746.497431 | 20.7936696 | 3 | 12.0052307 |
| ACE | Hyperarid dolomite | 314.743324 | 100.4297817 | 12 | 28.9915808 |
| ACE | Hyperarid dust | 311.050796 | 182.7650780 | 2 | 129.2344260 |
| ACE | Hyperarid loess soil | 466.260376 | 42.5794120 | 3 | 24.5832350 |
| Shannon | Arid limestone | 3.782559 | 1.0939745 | 12 | 0.3158033 |
| Shannon | Arid dust | 2.997964 | 1.5634435 | 2 | 1.1055215 |
| Shannon | Arid loess soil | 5.594932 | 0.0319481 | 3 | 0.0184452 |
| Shannon | Hyperarid dolomite | 3.328207 | 0.2683934 | 12 | 0.0774785 |
| Shannon | Hyperarid dust | 1.483307 | 0.8891069 | 2 | 0.6286935 |
| Shannon | Hyperarid loess soil | 3.779445 | 0.8513243 | 3 | 0.4915123 |
Beta diversity
Calculate and plot beta diversity mertrics.
1. Possible changes in biofilm arid vs hyper arid
Is there a difference between the two sites. However, since we know that that samples are of different nature we’ll have to control for rock type, source and location:
(mod1 <- adonis(
otu_table(Rock_weathering_filt3_GMPR) ~ Climate * Source * Location,
data = as(sample_data(Rock_weathering_filt3_GMPR), "data.frame"),
method = "horn",
permutations = 9999
))##
## Call:
## adonis(formula = otu_table(Rock_weathering_filt3_GMPR) ~ Climate * Source * Location, data = as(sample_data(Rock_weathering_filt3_GMPR), "data.frame"), permutations = 9999, method = "horn")
##
## Permutation: free
## Number of permutations: 9999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Climate 1 2.5299 2.52988 21.3820 0.21632 0.0001 ***
## Source 3 4.7060 1.56865 13.2579 0.40238 0.0001 ***
## Location 1 0.4870 0.48696 4.1157 0.04164 0.0033 **
## Climate:Source 1 0.4397 0.43972 3.7164 0.03760 0.0026 **
## Climate:Location 1 0.4605 0.46052 3.8922 0.03938 0.0056 **
## Source:Location 1 0.1142 0.11421 0.9653 0.00977 0.4812
## Residuals 25 2.9580 0.11832 0.25292
## Total 33 11.6952 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Rock_weathering_filt3_GMPR_Arid <- subset_samples(Rock_weathering_filt3_GMPR, Climate == "Arid")
Rock_weathering_filt3_GMPR_Arid <- filter_taxa(Rock_weathering_filt3_GMPR_Arid, function(x) sum(x) > 0, TRUE)
(mod2 <- adonis(
otu_table(Rock_weathering_filt3_GMPR_Arid) ~ Source * Location,
data = as(sample_data(Rock_weathering_filt3_GMPR_Arid), "data.frame"),
method = "horn",
permutations = 9999
))##
## Call:
## adonis(formula = otu_table(Rock_weathering_filt3_GMPR_Arid) ~ Source * Location, data = as(sample_data(Rock_weathering_filt3_GMPR_Arid), "data.frame"), permutations = 9999, method = "horn")
##
## Permutation: free
## Number of permutations: 9999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Source 2 2.3058 1.15288 7.3438 0.47881 0.0001 ***
## Location 1 0.4691 0.46905 2.9878 0.09740 0.0132 *
## Residuals 13 2.0408 0.15699 0.42379
## Total 16 4.8156 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Rock_weathering_filt3_GMPR_Hyperarid <- subset_samples(Rock_weathering_filt3_GMPR, Climate == "Hyperarid")
Rock_weathering_filt3_GMPR_Hyperarid <- filter_taxa(Rock_weathering_filt3_GMPR_Hyperarid, function(x) sum(x) > 0, TRUE)
(mod3 <- adonis(
otu_table(Rock_weathering_filt3_GMPR_Hyperarid) ~ Source * Location,
data = as(sample_data(Rock_weathering_filt3_GMPR_Hyperarid), "data.frame"),
method = "horn",
permutations = 9999
))##
## Call:
## adonis(formula = otu_table(Rock_weathering_filt3_GMPR_Hyperarid) ~ Source * Location, data = as(sample_data(Rock_weathering_filt3_GMPR_Hyperarid), "data.frame"), permutations = 9999, method = "horn")
##
## Permutation: free
## Number of permutations: 9999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Source 2 2.8454 1.42270 18.6150 0.65416 0.0001 ***
## Location 1 0.4729 0.47295 6.1882 0.10873 0.0102 *
## Source:Location 1 0.1142 0.11421 1.4944 0.02626 0.2334
## Residuals 12 0.9171 0.07643 0.21085
## Total 16 4.3497 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1According to this model we see that indeed there’s an effect of site on the community (p = 0.001), and that effect accounts for about 17% of the variance. Also, considering that Location is only borderline significant and explains very little of the data, we could probably take it out of the model to make a minimal adequate model.
(mod4 <- adonis(
otu_table(Rock_weathering_filt3_GMPR) ~ Climate * Source,
data = as(sample_data(Rock_weathering_filt3_GMPR), "data.frame"),
method = "horn",
permutations = 9999
))##
## Call:
## adonis(formula = otu_table(Rock_weathering_filt3_GMPR) ~ Climate * Source, data = as(sample_data(Rock_weathering_filt3_GMPR), "data.frame"), permutations = 9999, method = "horn")
##
## Permutation: free
## Number of permutations: 9999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Climate 1 2.5299 2.52988 17.6466 0.21632 1e-04 ***
## Source 3 4.7060 1.56865 10.9418 0.40238 1e-04 ***
## Climate:Source 1 0.4452 0.44521 3.1055 0.03807 8e-03 **
## Residuals 28 4.0142 0.14336 0.34323
## Total 33 11.6952 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1(mod5 <- adonis(
otu_table(Rock_weathering_filt3_GMPR_Arid) ~ Source,
data = as(sample_data(Rock_weathering_filt3_GMPR_Arid), "data.frame"),
method = "horn",
permutations = 9999
))##
## Call:
## adonis(formula = otu_table(Rock_weathering_filt3_GMPR_Arid) ~ Source, data = as(sample_data(Rock_weathering_filt3_GMPR_Arid), "data.frame"), permutations = 9999, method = "horn")
##
## Permutation: free
## Number of permutations: 9999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Source 2 2.3058 1.15288 6.4307 0.47881 2e-04 ***
## Residuals 14 2.5099 0.17928 0.52119
## Total 16 4.8156 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1(mod6 <- adonis(
otu_table(Rock_weathering_filt3_GMPR_Hyperarid) ~ Source,
data = as(sample_data(Rock_weathering_filt3_GMPR_Hyperarid), "data.frame"),
method = "horn",
permutations = 9999
))##
## Call:
## adonis(formula = otu_table(Rock_weathering_filt3_GMPR_Hyperarid) ~ Source, data = as(sample_data(Rock_weathering_filt3_GMPR_Hyperarid), "data.frame"), permutations = 9999, method = "horn")
##
## Permutation: free
## Number of permutations: 9999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Source 2 2.8454 1.42270 13.241 0.65416 2e-04 ***
## Residuals 14 1.5043 0.10745 0.34584
## Total 16 4.3497 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Final model
print(mod4)##
## Call:
## adonis(formula = otu_table(Rock_weathering_filt3_GMPR) ~ Climate * Source, data = as(sample_data(Rock_weathering_filt3_GMPR), "data.frame"), permutations = 9999, method = "horn")
##
## Permutation: free
## Number of permutations: 9999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Climate 1 2.5299 2.52988 17.6466 0.21632 1e-04 ***
## Source 3 4.7060 1.56865 10.9418 0.40238 1e-04 ***
## Climate:Source 1 0.4452 0.44521 3.1055 0.03807 8e-03 **
## Residuals 28 4.0142 0.14336 0.34323
## Total 33 11.6952 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1mod4_pairwise <- PairwiseAdonis(
otu_table(Rock_weathering_filt3_GMPR),
sample_data(Rock_weathering_filt3_GMPR)$Climate.Source,
sim.function = "vegdist",
sim.method = "horn",
p.adjust.m = "BH"
)
print(mod4_pairwise)## pairs total.DF F.Model R2 p.value
## 1 Arid dust vs Hyperarid loess soil 4 4.803200 0.6155424 0.1000000
## 2 Arid dust vs Hyperarid dust 3 2.992098 0.5993669 0.3333333
## 3 Arid dust vs Arid limestone 13 4.606432 0.2773884 0.0117000
## 4 Arid dust vs Hyperarid dolomite 13 6.594183 0.3546369 0.0119000
## 5 Arid dust vs Arid loess soil 4 5.363663 0.6413055 0.1000000
## 6 Hyperarid loess soil vs Hyperarid dust 4 10.866238 0.7836472 0.2000000
## 7 Hyperarid loess soil vs Arid limestone 14 5.593760 0.3008407 0.0070000
## 8 Hyperarid loess soil vs Hyperarid dolomite 14 16.022459 0.5520710 0.0026000
## 9 Hyperarid loess soil vs Arid loess soil 5 95.510242 0.9598031 0.1000000
## 10 Hyperarid dust vs Arid limestone 13 5.343419 0.3080949 0.0313000
## 11 Hyperarid dust vs Hyperarid dolomite 13 11.619149 0.4919377 0.0094000
## 12 Hyperarid dust vs Arid loess soil 4 205.847549 0.9856355 0.1000000
## 13 Arid limestone vs Hyperarid dolomite 23 21.900583 0.4988677 0.0001000
## 14 Arid limestone vs Arid loess soil 14 9.112582 0.4120994 0.0018000
## 15 Hyperarid dolomite vs Arid loess soil 14 16.841743 0.5643686 0.0032000
## p.adjusted sig
## 1 0.11538462
## 2 0.33333333
## 3 0.02231250 .
## 4 0.02231250 .
## 5 0.11538462
## 6 0.21428571
## 7 0.02100000 .
## 8 0.01200000 .
## 9 0.11538462
## 10 0.05216667
## 11 0.02231250 .
## 12 0.11538462
## 13 0.00150000 *
## 14 0.01200000 .
## 15 0.01200000 .sig_pairs <- as.character(mod4_pairwise$pairs[mod4_pairwise$p.adjusted < 0.05])
simper(otu_table(Rock_weathering_filt3_GMPR), sample_data(Rock_weathering_filt3_GMPR)$Climate.Source, parallel = 4)## cumulative contributions of most influential species:
##
## $`Arid dust_Hyperarid loess soil`
## OTU6 OTU65 OTU838 OTU90 OTU596 OTU11 OTU187 OTU93 OTU746
## 0.2223516 0.3677866 0.4056901 0.4304991 0.4528999 0.4693262 0.4837166 0.4963321 0.5089143
## OTU167 OTU711 OTU121 OTU144 OTU99 OTU194 OTU105 OTU356 OTU340
## 0.5206772 0.5323162 0.5428037 0.5526841 0.5624404 0.5714645 0.5787230 0.5859611 0.5926902
## OTU115 OTU640 OTU55 OTU298 OTU715 OTU88 OTU197 OTU48 OTU221
## 0.5992910 0.6058761 0.6123625 0.6187386 0.6250711 0.6313974 0.6376038 0.6433882 0.6488153
## OTU301 OTU1047 OTU322 OTU16 OTU586 OTU46 OTU172 OTU386 OTU67
## 0.6541319 0.6593526 0.6645414 0.6694651 0.6739228 0.6782800 0.6825173 0.6866930 0.6908583
## OTU333 OTU854 OTU883
## 0.6945572 0.6981826 0.7016897
##
## $`Arid dust_Hyperarid dust`
## OTU6 OTU65 OTU838 OTU55
## 0.4922822 0.6517818 0.6971512 0.7299602
##
## $`Arid dust_Arid limestone`
## OTU6 OTU65 OTU20 OTU40 OTU838 OTU225 OTU422 OTU596 OTU73
## 0.1123216 0.2223095 0.2918201 0.3566264 0.3846318 0.4019503 0.4188998 0.4354418 0.4510851
## OTU46 OTU11 OTU388 OTU119 OTU29 OTU18 OTU34 OTU746 OTU711
## 0.4642743 0.4766768 0.4880677 0.4986328 0.5091612 0.5193871 0.5289518 0.5383129 0.5469937
## OTU16 OTU235 OTU854 OTU174 OTU299 OTU194 OTU68 OTU545 OTU37
## 0.5556579 0.5642893 0.5727110 0.5809073 0.5890471 0.5960083 0.6028022 0.6094612 0.6160857
## OTU925 OTU41 OTU115 OTU140 OTU356 OTU60 OTU137 OTU197 OTU166
## 0.6225697 0.6287094 0.6345215 0.6403150 0.6457386 0.6510360 0.6562074 0.6613174 0.6663109
## OTU605 OTU69 OTU55 OTU1089 OTU27 OTU218 OTU48 OTU45
## 0.6712870 0.6762240 0.6810537 0.6854239 0.6895759 0.6936273 0.6976757 0.7016868
##
## $`Arid dust_Hyperarid dolomite`
## OTU1 OTU2 OTU3 OTU7 OTU5 OTU65 OTU9 OTU18 OTU8
## 0.1527495 0.2363471 0.3165709 0.3820169 0.4147143 0.4454892 0.4704565 0.4947935 0.5170144
## OTU17 OTU41 OTU16 OTU11 OTU936 OTU20 OTU28 OTU12 OTU10
## 0.5378908 0.5586779 0.5762334 0.5933941 0.6090247 0.6239327 0.6383626 0.6526239 0.6663706
## OTU6 OTU15 OTU33 OTU14
## 0.6767124 0.6869395 0.6964878 0.7058854
##
## $`Arid dust_Arid loess soil`
## OTU65 OTU25 OTU88 OTU62 OTU11 OTU838 OTU68 OTU596
## 0.07600412 0.09862665 0.12048117 0.14055785 0.15967703 0.17826462 0.19067874 0.20145631
## OTU177 OTU78 OTU133 OTU144 OTU46 OTU115 OTU67 OTU16
## 0.21192993 0.22239646 0.23195007 0.24115456 0.25014254 0.25870758 0.26685638 0.27420872
## OTU156 OTU388 OTU116 OTU137 OTU197 OTU100 OTU91 OTU687
## 0.28151396 0.28845987 0.29497148 0.30133776 0.30763580 0.31387308 0.31978477 0.32560209
## OTU746 OTU711 OTU73 OTU412 OTU194 OTU422 OTU160 OTU218
## 0.33120586 0.33673375 0.34225822 0.34731488 0.35212805 0.35690940 0.36151216 0.36607842
## OTU152 OTU53 OTU131 OTU135 OTU356 OTU125 OTU163 OTU37
## 0.37046694 0.37485116 0.37885223 0.38274887 0.38655530 0.39035864 0.39395340 0.39753331
## OTU556 OTU311 OTU130 OTU214 OTU227 OTU240 OTU341 OTU233
## 0.40109574 0.40463803 0.40814028 0.41156987 0.41498306 0.41836142 0.42169721 0.42499571
## OTU854 OTU648 OTU101 OTU92 OTU55 OTU716 OTU220 OTU139
## 0.42827522 0.43153536 0.43476556 0.43797394 0.44115558 0.44428602 0.44740514 0.45049071
## OTU140 OTU278 OTU20 OTU103 OTU60 OTU253 OTU171 OTU1235
## 0.45356227 0.45649184 0.45941847 0.46234298 0.46512485 0.46790496 0.47063934 0.47334811
## OTU107 OTU365 OTU294 OTU301 OTU647 OTU206 OTU136 OTU231
## 0.47601073 0.47866935 0.48128416 0.48386623 0.48644557 0.48901761 0.49157714 0.49411399
## OTU479 OTU1050 OTU1288 OTU82 OTU248 OTU360 OTU181 OTU434
## 0.49659608 0.49905562 0.50151265 0.50394266 0.50636675 0.50871712 0.51105399 0.51337992
## OTU945 OTU638 OTU1102 OTU315 OTU1184 OTU457 OTU44 OTU491
## 0.51570002 0.51801894 0.52031981 0.52260630 0.52487989 0.52712302 0.52935591 0.53158523
## OTU472 OTU938 OTU184 OTU471 OTU182 OTU538 OTU352 OTU169
## 0.53380813 0.53602510 0.53823459 0.54042578 0.54253898 0.54464035 0.54673555 0.54882651
## OTU586 OTU191 OTU190 OTU751 OTU304 OTU251 OTU164 OTU667
## 0.55091733 0.55300683 0.55508810 0.55712859 0.55915785 0.56117530 0.56318056 0.56518015
## OTU69 OTU619 OTU170 OTU154 OTU992 OTU1013 OTU707 OTU302
## 0.56717714 0.56916832 0.57115385 0.57313482 0.57509789 0.57705219 0.57900145 0.58092857
## OTU725 OTU430 OTU1116 OTU132 OTU466 OTU364 OTU489 OTU166
## 0.58282302 0.58471488 0.58660087 0.58848567 0.59036511 0.59223482 0.59409227 0.59593839
## OTU33 OTU1218 OTU421 OTU1319 OTU545 OTU309 OTU456 OTU562
## 0.59778312 0.59962319 0.60146290 0.60326476 0.60505799 0.60683960 0.60859329 0.61034045
## OTU635 OTU468 OTU610 OTU347 OTU833 OTU582 OTU41 OTU333
## 0.61206287 0.61376517 0.61546266 0.61714945 0.61882254 0.62049477 0.62216179 0.62381474
## OTU6 OTU27 OTU86 OTU451 OTU162 OTU561 OTU439 OTU579
## 0.62545802 0.62705709 0.62865264 0.63024097 0.63181778 0.63338508 0.63491568 0.63644400
## OTU255 OTU653 OTU279 OTU368 OTU783 OTU93 OTU298 OTU323
## 0.63797113 0.63949107 0.64100660 0.64251744 0.64400822 0.64549236 0.64697241 0.64843615
## OTU1024 OTU1005 OTU389 OTU799 OTU348 OTU543 OTU524 OTU258
## 0.64988797 0.65133108 0.65277123 0.65418616 0.65559921 0.65699846 0.65838960 0.65977752
## OTU953 OTU929 OTU174 OTU1100 OTU999 OTU705 OTU199 OTU1049
## 0.66116274 0.66251874 0.66386908 0.66520644 0.66653398 0.66783135 0.66912435 0.67041127
## OTU1242 OTU449 OTU303 OTU232 OTU765 OTU499 OTU10 OTU1011
## 0.67169466 0.67295718 0.67420516 0.67544956 0.67668808 0.67792553 0.67913601 0.68034452
## OTU595 OTU697 OTU548 OTU353 OTU310 OTU28 OTU1170 OTU404
## 0.68154983 0.68274660 0.68394090 0.68513515 0.68632833 0.68750914 0.68868372 0.68984795
## OTU141 OTU277 OTU469 OTU883 OTU557 OTU95 OTU29 OTU1108
## 0.69101177 0.69216462 0.69330654 0.69444839 0.69558676 0.69671804 0.69784612 0.69896224
## OTU641
## 0.70007721
##
## $`Hyperarid loess soil_Hyperarid dust`
## OTU6 OTU55 OTU90 OTU11 OTU94 OTU187 OTU93 OTU167 OTU121
## 0.4547139 0.4932104 0.5179318 0.5361943 0.5506355 0.5649686 0.5775294 0.5893159 0.5997621
## OTU99 OTU144 OTU9 OTU105 OTU340 OTU298 OTU115 OTU640 OTU715
## 0.6095933 0.6193616 0.6270627 0.6343667 0.6410613 0.6476927 0.6542631 0.6608232 0.6672825
## OTU88 OTU197 OTU221 OTU322 OTU1047 OTU16 OTU484
## 0.6735780 0.6797532 0.6851539 0.6903247 0.6951534 0.6999152 0.7044646
##
## $`Hyperarid loess soil_Arid limestone`
## OTU6 OTU20 OTU40 OTU90 OTU225 OTU11 OTU73 OTU422 OTU119
## 0.2070926 0.2740292 0.3382045 0.3562460 0.3732290 0.3885619 0.4034598 0.4178979 0.4286859
## OTU29 OTU187 OTU46 OTU388 OTU18 OTU167 OTU34 OTU93 OTU299
## 0.4392449 0.4494930 0.4594962 0.4689023 0.4780078 0.4868025 0.4953853 0.5038895 0.5120122
## OTU235 OTU121 OTU174 OTU99 OTU925 OTU854 OTU545 OTU144 OTU37
## 0.5198446 0.5274965 0.5347188 0.5412268 0.5476305 0.5539799 0.5600864 0.5661491 0.5721572
## OTU197 OTU140 OTU68 OTU16 OTU340 OTU298 OTU605 OTU640 OTU69
## 0.5779404 0.5835652 0.5888852 0.5940274 0.5989837 0.6039133 0.6088284 0.6136372 0.6183414
## OTU715 OTU105 OTU41 OTU137 OTU88 OTU27 OTU1089 OTU60 OTU45
## 0.6230415 0.6277280 0.6323451 0.6367965 0.6411466 0.6453214 0.6494391 0.6534993 0.6575361
## OTU221 OTU166 OTU218 OTU261 OTU163 OTU81 OTU115 OTU322 OTU259
## 0.6615384 0.6654843 0.6693905 0.6731251 0.6767907 0.6804246 0.6836906 0.6868900 0.6900614
## OTU1047 OTU172 OTU939 OTU67
## 0.6931820 0.6962866 0.6993799 0.7024051
##
## $`Hyperarid loess soil_Hyperarid dolomite`
## OTU1 OTU2 OTU3 OTU7 OTU6 OTU5 OTU9 OTU18 OTU8
## 0.1495788 0.2321174 0.3117152 0.3744107 0.4214125 0.4537922 0.4782802 0.5014206 0.5233406
## OTU17 OTU41 OTU16 OTU936 OTU11 OTU28 OTU12 OTU20 OTU10
## 0.5440341 0.5638310 0.5798466 0.5953299 0.6102815 0.6246998 0.6387122 0.6525475 0.6660844
## OTU15 OTU14 OTU13 OTU45
## 0.6762133 0.6855007 0.6942967 0.7030461
##
## $`Hyperarid loess soil_Arid loess soil`
## OTU6 OTU25 OTU62 OTU88 OTU90 OTU11 OTU68 OTU177 OTU78
## 0.1171964 0.1409598 0.1605516 0.1798885 0.1922238 0.2043007 0.2156943 0.2264171 0.2370192
## OTU133 OTU156 OTU187 OTU46 OTU116 OTU67 OTU137 OTU100 OTU687
## 0.2468319 0.2543986 0.2615122 0.2685441 0.2751451 0.2813878 0.2874536 0.2934299 0.2993293
## OTU388 OTU115 OTU121 OTU73 OTU412 OTU91 OTU167 OTU93 OTU16
## 0.3051050 0.3105215 0.3159249 0.3213181 0.3265045 0.3316752 0.3368401 0.3418967 0.3469359
## OTU99 OTU160 OTU218 OTU144 OTU131 OTU135 OTU53 OTU556 OTU152
## 0.3517643 0.3565360 0.3611535 0.3655646 0.3697266 0.3738519 0.3776564 0.3814527 0.3852354
## OTU130 OTU125 OTU227 OTU340 OTU214 OTU233 OTU640 OTU341 OTU311
## 0.3888725 0.3924354 0.3958973 0.3993476 0.4027687 0.4061872 0.4095931 0.4129866 0.4163480
## OTU648 OTU422 OTU716 OTU105 OTU197 OTU163 OTU140 OTU44 OTU37
## 0.4196998 0.4230194 0.4263048 0.4295726 0.4328230 0.4359927 0.4391577 0.4422934 0.4454224
## OTU92 OTU240 OTU715 OTU139 OTU253 OTU221 OTU298 OTU101 OTU171
## 0.4484404 0.4514561 0.4544639 0.4573782 0.4602606 0.4631238 0.4659602 0.4687870 0.4716058
## OTU1235 OTU107 OTU365 OTU491 OTU103 OTU647 OTU479 OTU1047 OTU1288
## 0.4744165 0.4771309 0.4797889 0.4824360 0.4850823 0.4876817 0.4902569 0.4928293 0.4953810
## OTU278 OTU322 OTU136 OTU1102 OTU1050 OTU206 OTU360 OTU220 OTU248
## 0.4979283 0.5004747 0.5030146 0.5055356 0.5080079 0.5104620 0.5128571 0.5152382 0.5176119
## OTU231 OTU48 OTU638 OTU945 OTU315 OTU294 OTU181 OTU190 OTU471
## 0.5199597 0.5222842 0.5245825 0.5268765 0.5291585 0.5314373 0.5337155 0.5359310 0.5381211
## OTU172 OTU169 OTU457 OTU82 OTU191 OTU1013 OTU472 OTU182 OTU251
## 0.5403020 0.5424802 0.5446509 0.5468014 0.5489293 0.5510565 0.5531619 0.5552609 0.5573352
## OTU619 OTU184 OTU352 OTU938 OTU154 OTU707 OTU304 OTU538 OTU386
## 0.5594017 0.5614639 0.5635207 0.5655628 0.5675880 0.5696106 0.5716293 0.5736343 0.5756356
## OTU1116 OTU582 OTU751 OTU434 OTU20 OTU1319 OTU1218 OTU992 OTU309
## 0.5776195 0.5795812 0.5815142 0.5834436 0.5853509 0.5872476 0.5891360 0.5910228 0.5929026
## OTU456 OTU364 OTU302 OTU69 OTU468 OTU725 OTU86 OTU466 OTU562
## 0.5947562 0.5965751 0.5983597 0.6001340 0.6019002 0.6036514 0.6053964 0.6071295 0.6088489
## OTU1184 OTU347 OTU430 OTU164 OTU60 OTU451 OTU635 OTU489 OTU854
## 0.6105629 0.6122639 0.6139634 0.6156620 0.6173343 0.6189824 0.6206193 0.6222384 0.6238543
## OTU610 OTU132 OTU439 OTU255 OTU1046 OTU421 OTU170 OTU543 OTU579
## 0.6254567 0.6270446 0.6286319 0.6302162 0.6317761 0.6333278 0.6348784 0.6363976 0.6379151
## OTU1024 OTU27 OTU323 OTU929 OTU368 OTU953 OTU799 OTU545 OTU258
## 0.6394210 0.6409169 0.6423915 0.6438630 0.6453326 0.6467955 0.6482429 0.6496722 0.6510934
## OTU1282 OTU267 OTU28 OTU1100 OTU277 OTU279 OTU667 OTU199 OTU705
## 0.6524991 0.6538874 0.6552578 0.6566268 0.6579884 0.6593460 0.6606965 0.6620386 0.6633559
## OTU999 OTU232 OTU95 OTU303 OTU836 OTU1242 OTU653 OTU3 OTU141
## 0.6646697 0.6659821 0.6672939 0.6685879 0.6698524 0.6711151 0.6723738 0.6736310 0.6748814
## OTU697 OTU273 OTU499 OTU29 OTU1011 OTU389 OTU162 OTU1108 OTU166
## 0.6761230 0.6773607 0.6785955 0.6798301 0.6810609 0.6822766 0.6834907 0.6847033 0.6859052
## OTU595 OTU524 OTU185 OTU765 OTU41 OTU404 OTU641 OTU557 OTU1005
## 0.6871070 0.6883061 0.6894992 0.6906885 0.6918492 0.6930093 0.6941662 0.6953181 0.6964525
## OTU1049 OTU10 OTU1101 OTU449
## 0.6975821 0.6987054 0.6998284 0.7009432
##
## $`Hyperarid dust_Arid limestone`
## OTU6 OTU20 OTU40 OTU55 OTU225 OTU422 OTU73 OTU11 OTU46
## 0.3830030 0.4418277 0.4965700 0.5241168 0.5387383 0.5528407 0.5659891 0.5778580 0.5889906
## OTU94 OTU388 OTU29 OTU119 OTU18 OTU34 OTU235 OTU16 OTU854
## 0.5996827 0.6092874 0.6183609 0.6273489 0.6359934 0.6440744 0.6514858 0.6587135 0.6657503
## OTU174 OTU299 OTU68 OTU545 OTU9 OTU37
## 0.6726643 0.6795149 0.6852951 0.6910019 0.6966084 0.7021179
##
## $`Hyperarid dust_Hyperarid dolomite`
## OTU6 OTU1 OTU2 OTU3 OTU7 OTU5 OTU18 OTU9 OTU8
## 0.1443995 0.2811765 0.3569750 0.4302446 0.4872402 0.5170148 0.5383456 0.5591551 0.5792831
## OTU17 OTU41 OTU11 OTU16 OTU936 OTU28 OTU20 OTU12
## 0.5983333 0.6167381 0.6326139 0.6480207 0.6622816 0.6756378 0.6885866 0.7014489
##
## $`Hyperarid dust_Arid loess soil`
## OTU6 OTU55 OTU25 OTU88 OTU11 OTU62 OTU68 OTU177 OTU78
## 0.2717856 0.2922788 0.3102694 0.3274254 0.3433223 0.3590820 0.3688505 0.3770737 0.3852909
## OTU94 OTU133 OTU144 OTU46 OTU115 OTU67 OTU156 OTU16 OTU388
## 0.3931965 0.4006953 0.4078863 0.4149436 0.4216680 0.4280641 0.4338043 0.4395276 0.4449791
## OTU116 OTU137 OTU197 OTU100 OTU687 OTU91 OTU73 OTU412 OTU218
## 0.4500254 0.4550249 0.4599696 0.4648655 0.4695628 0.4742035 0.4785067 0.4824756 0.4864272
## OTU9 OTU422 OTU160 OTU53 OTU152 OTU125 OTU135 OTU131 OTU311
## 0.4902632 0.4938776 0.4974534 0.5009364 0.5043806 0.5077056 0.5109572 0.5141610 0.5171316
## OTU556 OTU163 OTU130 OTU484 OTU214 OTU227 OTU240 OTU37 OTU341
## 0.5200525 0.5228985 0.5256494 0.5283896 0.5310817 0.5337605 0.5364127 0.5390503 0.5416516
## OTU233 OTU854 OTU101 OTU648 OTU140 OTU716 OTU92 OTU220 OTU139
## 0.5442391 0.5467798 0.5493151 0.5518255 0.5543313 0.5568296 0.5592989 0.5617475 0.5641704
## OTU44 OTU491 OTU20 OTU278 OTU103 OTU253 OTU60 OTU1235 OTU206
## 0.5665825 0.5689943 0.5713301 0.5736308 0.5759258 0.5781072 0.5802428 0.5823693 0.5844926
## OTU171 OTU107 OTU365 OTU294 OTU647 OTU136 OTU231 OTU1050 OTU1102
## 0.5865914 0.5886821 0.5907679 0.5928210 0.5948466 0.5968391 0.5988303 0.6007964 0.6027482
## OTU479 OTU1288 OTU82 OTU248 OTU434 OTU638 OTU360 OTU945 OTU181
## 0.6046967 0.6066266 0.6085347 0.6104021 0.6122693 0.6140896 0.6158867 0.6176719 0.6194314
## OTU457 OTU472 OTU938 OTU1184 OTU184 OTU315 OTU33 OTU667 OTU471
## 0.6211887 0.6229334 0.6246741 0.6264112 0.6281455 0.6298719 0.6315934 0.6333143 0.6350343
## OTU190 OTU1013 OTU182 OTU538 OTU352 OTU191 OTU86 OTU169 OTU586
## 0.6367102 0.6383722 0.6400307 0.6416807 0.6433256 0.6449655 0.6465947 0.6482236 0.6498470
## OTU304 OTU251 OTU164 OTU619 OTU170 OTU751 OTU1116 OTU707 OTU582
## 0.6514406 0.6530247 0.6545987 0.6561620 0.6577205 0.6592749 0.6608170 0.6623473 0.6638686
## OTU302 OTU154 OTU69 OTU466 OTU725 OTU430 OTU132 OTU545 OTU489
## 0.6653819 0.6668883 0.6683903 0.6698868 0.6713743 0.6728597 0.6743389 0.6758092 0.6772674
## OTU166 OTU48 OTU992 OTU421 OTU1218 OTU1319 OTU456 OTU309 OTU364
## 0.6787234 0.6801775 0.6816228 0.6830672 0.6845111 0.6859461 0.6873638 0.6887794 0.6901816
## OTU635 OTU437 OTU610 OTU562 OTU298 OTU468 OTU347 OTU333
## 0.6915755 0.6929580 0.6943383 0.6957096 0.6970803 0.6984166 0.6997406 0.7010381
##
## $`Arid limestone_Hyperarid dolomite`
## OTU1 OTU2 OTU3 OTU6 OTU7 OTU5 OTU9 OTU8 OTU18
## 0.1440378 0.2242599 0.3019930 0.3607941 0.4191722 0.4507340 0.4744385 0.4957463 0.5163290
## OTU17 OTU41 OTU40 OTU11 OTU936 OTU16 OTU28 OTU12 OTU20
## 0.5365375 0.5544474 0.5699925 0.5851556 0.6002748 0.6145844 0.6287286 0.6422857 0.6555544
## OTU10 OTU15 OTU14 OTU13 OTU33
## 0.6687285 0.6786126 0.6876539 0.6962099 0.7045072
##
## $`Arid limestone_Arid loess soil`
## OTU6 OTU40 OTU20 OTU25 OTU88 OTU62 OTU11 OTU225
## 0.09760135 0.13750741 0.17679560 0.19728621 0.21638051 0.23340169 0.24845894 0.25799747
## OTU177 OTU78 OTU133 OTU73 OTU68 OTU119 OTU144 OTU67
## 0.26735411 0.27646905 0.28479143 0.29307840 0.30008547 0.30708372 0.31394618 0.32068762
## OTU156 OTU422 OTU18 OTU29 OTU34 OTU100 OTU116 OTU91
## 0.32725867 0.33341009 0.33935962 0.34499496 0.35056556 0.35599894 0.36127683 0.36643832
## OTU687 OTU46 OTU299 OTU388 OTU235 OTU197 OTU115 OTU218
## 0.37138684 0.37623323 0.38103236 0.38580627 0.39057213 0.39516382 0.39952480 0.40388431
## OTU412 OTU160 OTU174 OTU53 OTU152 OTU925 OTU135 OTU137
## 0.40821727 0.41229126 0.41633319 0.42024901 0.42416096 0.42806201 0.43172259 0.43528646
## OTU37 OTU854 OTU545 OTU163 OTU125 OTU130 OTU131 OTU556
## 0.43864358 0.44196239 0.44519165 0.44838886 0.45155916 0.45469790 0.45781670 0.46088453
## OTU605 OTU311 OTU69 OTU140 OTU341 OTU214 OTU27 OTU233
## 0.46394974 0.46699883 0.46996880 0.47291024 0.47584516 0.47876919 0.48167258 0.48457197
## OTU648 OTU240 OTU16 OTU92 OTU44 OTU491 OTU139 OTU220
## 0.48743615 0.49029565 0.49310940 0.49591666 0.49871621 0.50143167 0.50413985 0.50682390
## OTU227 OTU278 OTU101 OTU1089 OTU103 OTU171 OTU45 OTU253
## 0.50944639 0.51205855 0.51465647 0.51723675 0.51980481 0.52223855 0.52464256 0.52704332
## OTU107 OTU261 OTU365 OTU206 OTU231 OTU1235 OTU41 OTU479
## 0.52941636 0.53174517 0.53406670 0.53634794 0.53861594 0.54087696 0.54311887 0.54534212
## OTU1288 OTU1102 OTU81 OTU1050 OTU294 OTU181 OTU60 OTU945
## 0.54753509 0.54972106 0.55189155 0.55399097 0.55607199 0.55813344 0.56016996 0.56216653
## OTU82 OTU48 OTU360 OTU472 OTU471 OTU136 OTU169 OTU248
## 0.56416015 0.56614595 0.56813109 0.57010003 0.57205708 0.57400361 0.57593588 0.57786729
## OTU315 OTU184 OTU638 OTU182 OTU352 OTU716 OTU647 OTU191
## 0.57978907 0.58169843 0.58360105 0.58549541 0.58737150 0.58924686 0.59110929 0.59295132
## OTU349 OTU457 OTU938 OTU1013 OTU751 OTU619 OTU164 OTU166
## 0.59479300 0.59662297 0.59843435 0.60023859 0.60201582 0.60378025 0.60554228 0.60727629
## OTU251 OTU190 OTU154 OTU434 OTU707 OTU1116 OTU1218 OTU302
## 0.60900472 0.61070425 0.61239395 0.61407383 0.61574542 0.61739401 0.61904009 0.62068001
## OTU489 OTU421 OTU582 OTU286 OTU7 OTU259 OTU667 OTU364
## 0.62231663 0.62395222 0.62557826 0.62718663 0.62878593 0.63037948 0.63196953 0.63355404
## OTU456 OTU1319 OTU298 OTU992 OTU562 OTU304 OTU468 OTU610
## 0.63512882 0.63670199 0.63825851 0.63979366 0.64131334 0.64282784 0.64433016 0.64582281
## OTU170 OTU347 OTU939 OTU86 OTU430 OTU246 OTU239 OTU949
## 0.64730766 0.64879001 0.65025643 0.65166808 0.65307432 0.65445899 0.65584235 0.65720240
## OTU255 OTU439 OTU466 OTU132 OTU530 OTU579 OTU368 OTU362
## 0.65856178 0.65991755 0.66125549 0.66259309 0.66392616 0.66524781 0.66654856 0.66784058
## OTU28 OTU451 OTU653 OTU1299 OTU309 OTU333 OTU399 OTU323
## 0.66913171 0.67042259 0.67170906 0.67298919 0.67426714 0.67554482 0.67680684 0.67806486
## OTU953 OTU348 OTU162 OTU1024 OTU389 OTU1005 OTU929 OTU524
## 0.67931748 0.68056959 0.68181843 0.68303243 0.68424157 0.68543316 0.68662133 0.68779205
## OTU1100 OTU586 OTU95 OTU279 OTU799 OTU87 OTU1101 OTU232
## 0.68895982 0.69012713 0.69129203 0.69244608 0.69359440 0.69473764 0.69587999 0.69701440
## OTU725 OTU303 OTU999
## 0.69814422 0.69925971 0.70036244
##
## $`Hyperarid dolomite_Arid loess soil`
## OTU1 OTU2 OTU3 OTU7 OTU5 OTU9 OTU18 OTU8 OTU17
## 0.1319013 0.2062641 0.2786385 0.3307085 0.3600777 0.3818384 0.4015745 0.4212911 0.4401489
## OTU41 OTU936 OTU28 OTU12 OTU16 OTU10 OTU11 OTU20 OTU6
## 0.4571149 0.4711765 0.4842321 0.4967527 0.5091024 0.5210569 0.5325276 0.5434614 0.5531014
## OTU15 OTU14 OTU13 OTU24 OTU33 OTU45 OTU88 OTU25 OTU54
## 0.5622927 0.5706666 0.5785943 0.5859354 0.5932440 0.6005279 0.6077454 0.6147879 0.6216491
## OTU62 OTU34 OTU39 OTU47 OTU19 OTU22 OTU35 OTU68 OTU32
## 0.6280701 0.6340900 0.6399058 0.6455094 0.6510462 0.6560153 0.6601594 0.6640414 0.6677063
## OTU29 OTU756 OTU23 OTU37 OTU78 OTU26 OTU177 OTU30 OTU133
## 0.6713117 0.6748203 0.6783113 0.6817969 0.6852336 0.6886605 0.6920868 0.6953067 0.6984351
## OTU46
## 0.7015026GMPR_ord <- ordinate(Rock_weathering_filt3_GMPR, "CAP", "horn", formula = Rock_weathering_filt3_GMPR ~ Climate * Source)
explained <- eigenvals(GMPR_ord)/sum( eigenvals(GMPR_ord)) * 100
explained <- as.numeric(format(round(explained, 1), nsmall = 1))
data2plot <- cbind(scores(GMPR_ord, display = "sites"), sample_data(Rock_weathering_filt3_GMPR))
p_ord <- ggplot(data2plot) +
geom_point(aes(x = CAP1, y = CAP2, colour = Source, shape = Climate), size = 3, alpha = 2/3 ) +
scale_colour_manual(values = pom4) +
stat_ellipse(aes(x = CAP1, y = CAP2, group = Climate), color = "black", alpha = 0.5, type = "norm", level = 0.95, linetype = 2) +
xlab(label = paste0("CAP1", " (", explained[1],"%)")) +
ylab(label = paste0("CAP2", " (", explained[2],"%)")) +
coord_fixed(sqrt(explained[2] / explained[1]))
print(p_ord)
Taxonomic features
Explore and plot the taxonomic distribution of the sequences
Rock_weathering_filt3_GMPR_rel <- transform_sample_counts(Rock_weathering_filt3_GMPR, function(x) x / sum(x) )
Rock_weathering_filt3_GMPR_rel %>%
sample_data() %>%
arrange(Climate, Source) %>%
.$sample_names ->
Sample_order
Rock_weathering_filt3_100 <-
prune_taxa(names(sort(taxa_sums(Rock_weathering_filt3_GMPR_rel), TRUE)[1:100]), Rock_weathering_filt3_GMPR_rel)
plot_heatmap(
Rock_weathering_filt3_100,
method = NULL,
distance = NULL,
sample.label = "sample_title",
taxa.label = "Order",
sample_order = Sample_order,
low = "#000033",
high = "#FF3300"
) #+ theme_bw(base_size = 20) + theme(axis.text.x = element_text(hjust = 0, angle = -90.0))
Let’s look at the agglomerated taxa
Rock_weathering_filt3_glom <- tax_glom(Rock_weathering_filt3_GMPR,
"Phylum",
NArm = TRUE)
Rock_weathering_filt3_glom_rel <- transform_sample_counts(Rock_weathering_filt3_glom, function(x) x / sum(x))
Rock_weathering_filt3_glom_rel_DF <- psmelt(Rock_weathering_filt3_glom_rel)
Rock_weathering_filt3_glom_rel_DF$Phylum %<>% as.character()
# group dataframe by Phylum, calculate median rel. abundance
Rock_weathering_filt3_glom_rel_DF %>%
group_by(Phylum) %>%
summarise(median = median(Abundance)) ->
medians
# find Phyla whose rel. abund. is less than 0.5%
Rare_phyla <- medians[medians$median <= 0.005, ]$Phylum
# change their name to "Rare"
Rock_weathering_filt3_glom_rel_DF[Rock_weathering_filt3_glom_rel_DF$Phylum %in% Rare_phyla, ]$Phylum <- 'Rare'
# re-group
Rock_weathering_filt3_glom_rel_DF %>%
group_by(Sample, Climate, Phylum, Rock.type, Source) %>%
summarise(Abundance = sum(Abundance)) ->
Rock_weathering_filt3_glom_rel_DF_2plot
# ab.taxonomy$Freq <- sqrt(ab.taxonomy$Freq)
Rock_weathering_filt3_glom_rel_DF_2plot$Phylum %<>% sub("unclassified", "Unclassified", .)
Rock_weathering_filt3_glom_rel_DF_2plot$Phylum %<>% sub("uncultured", "Unclassified", .)
Rock_weathering_filt3_glom_rel_DF_2plot %>%
group_by(Sample) %>%
filter(Phylum == "Rare") %>%
summarise(`Rares (%)` = sum(Abundance * 100)) ->
Rares
# Percentage of reads classified as rare
Rares %>%
kable(., digits = 2, caption = "Percentage of reads per sample type classified as rare:") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = F)| Sample | Rares (%) |
|---|---|
| SbDust1S14 | 2.05 |
| SbDust2S31 | 0.43 |
| SbSlp1SNW49 | 0.82 |
| SbSlp2SNW50 | 0.91 |
| SbSlp3SNW51 | 0.85 |
| SbSlp4SNW52 | 1.99 |
| SbSlp5SNW53 | 0.72 |
| SbSlp6SNW54 | 1.59 |
| SbSoil1SA10 | 9.80 |
| SbSoil2SA11 | 10.41 |
| SbSoil3SA12 | 9.62 |
| SbWad1SNW55 | 2.90 |
| SbWad2SNW56 | 0.48 |
| SbWad3SNW57 | 0.42 |
| SbWad4SNW58 | 0.20 |
| SbWad5SNW59 | 0.47 |
| SbWad6SNW60 | 1.47 |
| UvDust1S32 | 1.60 |
| UvDust2S33 | 0.12 |
| UvSlp1GS70 | 1.09 |
| UvSlp2GS71 | 0.42 |
| UvSlp3CS25 | 0.77 |
| UvSlp3GS72 | 0.85 |
| UvSlp4GS73 | 0.64 |
| UvSlp5GS74 | 0.72 |
| UvSlp6GS75 | 0.34 |
| UvWad1GS76 | 9.37 |
| UvWad2CS23 | 2.17 |
| UvWad2GS77 | 0.75 |
| UvWad3CS27 | 2.96 |
| UvWad3GS78 | 0.16 |
| UvWad4GS79 | 0.77 |
| UvWad5GS80 | 0.30 |
| UvWad6GS81 | 0.38 |
sample_order <- match(Rares$Sample, row.names(sample_data(Rock_weathering_filt3_glom)))
Rares %<>% arrange(., sample_order)
Rares %>%
cbind(., sample_data(Rock_weathering_filt3_glom)) %>%
group_by(Climate.Source) %>%
summarise(`Rares (%)` = mean(`Rares (%)`)) ->
Rares_merged
# Percentage of reads classified as rare
Rares %>%
kable(., digits = 2, caption = "Percentage of reads per sample type classified as rare:") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = F)| Sample | Rares (%) |
|---|---|
| SbDust1S14 | 2.05 |
| UvWad2CS23 | 2.17 |
| UvSlp3CS25 | 0.77 |
| UvWad3CS27 | 2.96 |
| SbDust2S31 | 0.43 |
| UvDust1S32 | 1.60 |
| UvDust2S33 | 0.12 |
| SbSlp1SNW49 | 0.82 |
| SbSlp2SNW50 | 0.91 |
| SbSlp3SNW51 | 0.85 |
| SbSlp4SNW52 | 1.99 |
| SbSlp5SNW53 | 0.72 |
| SbSlp6SNW54 | 1.59 |
| SbWad1SNW55 | 2.90 |
| SbWad2SNW56 | 0.48 |
| SbWad3SNW57 | 0.42 |
| SbWad4SNW58 | 0.20 |
| SbWad5SNW59 | 0.47 |
| SbWad6SNW60 | 1.47 |
| UvSlp1GS70 | 1.09 |
| UvSlp2GS71 | 0.42 |
| UvSlp3GS72 | 0.85 |
| UvSlp4GS73 | 0.64 |
| UvSlp5GS74 | 0.72 |
| UvSlp6GS75 | 0.34 |
| UvWad1GS76 | 9.37 |
| UvWad2GS77 | 0.75 |
| UvWad3GS78 | 0.16 |
| UvWad4GS79 | 0.77 |
| UvWad5GS80 | 0.30 |
| UvWad6GS81 | 0.38 |
| SbSoil1SA10 | 9.80 |
| SbSoil2SA11 | 10.41 |
| SbSoil3SA12 | 9.62 |
Rock_weathering_filt3_glom_rel_DF_2plot %>%
group_by(Phylum) %>%
summarise(sum.Taxa = sum(Abundance)) %>%
arrange(desc(sum.Taxa)) -> Taxa_rank
Rock_weathering_filt3_glom_rel_DF_2plot$Phylum %<>%
factor(., levels = Taxa_rank$Phylum) %>%
fct_relevel(., "Rare", after = Inf)
p_taxa_box <-
ggplot(Rock_weathering_filt3_glom_rel_DF_2plot, aes(x = Phylum, y = (Abundance * 100))) +
geom_boxplot(aes(group = interaction(Phylum, Source)), position = position_dodge(width = 0.9), fatten = 1) +
geom_point(
aes(colour = Source),
position = position_jitterdodge(dodge.width = 1),
alpha = 1 / 2,
stroke = 0,
size = 2
) +
scale_colour_manual(values = pom4, name = "") +
theme_cowplot(font_size = 11, font_family = f_name) +
labs(x = NULL, y = "Relative abundance (%)") +
guides(colour = guide_legend(override.aes = list(size = 5))) +
facet_grid(Climate ~ .) +
background_grid(major = "xy",
minor = "none") +
theme(axis.text.x = element_text(
angle = 45,
vjust = 0.9,
hjust = 0.9
))
print(p_taxa_box)
Test differences between samples on the phylum level
Taxa_tests_phylum <- STAMPR(Rock_weathering_filt3_GMPR, "Phylum", sig_pairs)## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Phylum
## OTU1 "Proteobacteria"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 1118.38 11.2778 0.00078
## Source 3 412.13 4.1560 0.24511
## Climate:Source 1 2.40 0.0242 0.87637
## Residuals 28 1739.58
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 5, p-value = 0.2353
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -57.31870 77.10514
## sample estimates:
## difference in location
## -8.837742
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 13, p-value = 0.9273
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -45.05308 61.68040
## sample estimates:
## difference in location
## 3.711094
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 3, p-value = 0.03636
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -44.64033 -10.50860
## sample estimates:
## difference in location
## -24.33348
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 22, p-value = 0.6134
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -31.61913 23.25576
## sample estimates:
## difference in location
## 2.734424
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.03576
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -92.69994 -22.88429
## sample estimates:
## difference in location
## -41.7985
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 24, p-value = 0.006099
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -43.71818 -14.50227
## sample estimates:
## difference in location
## -31.94182
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 12, p-value = 0.4273
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -9.149286 9.089127
## sample estimates:
## difference in location
## -3.911288
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 3, p-value = 0.03636
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -43.181793 -5.626402
## sample estimates:
## difference in location
## -35.79785
##
## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Phylum
## OTU2 "Deinococcus-Thermus"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 212.5 2.1429 0.14323
## Source 3 1474.7 14.8713 0.00193
## Climate:Source 1 72.6 0.7321 0.39220
## Residuals 28 1512.7
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 24, p-value = 0.03576
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.1159525 28.8971507
## sample estimates:
## difference in location
## 3.763569
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.03576
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -16.468193 -6.626912
## sample estimates:
## difference in location
## -12.42899
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 18, p-value = 1
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -8.035956 20.819838
## sample estimates:
## difference in location
## 0.1319845
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 31, p-value = 0.0712
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.559324 12.947540
## sample estimates:
## difference in location
## 6.279289
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 24, p-value = 0.03576
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 6.553372 16.590053
## sample estimates:
## difference in location
## 11.95774
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 53, p-value = 0.2855
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -11.37397 7.94874
## sample estimates:
## difference in location
## -5.992007
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 35, p-value = 0.01724
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.1726432 24.9922558
## sample estimates:
## difference in location
## 2.545225
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -16.14293 -6.64311
## sample estimates:
## difference in location
## -11.97071
##
## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Phylum
## OTU5 "Bacteroidetes"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 12.97 0.1308 0.71761
## Source 3 821.88 8.2879 0.04042
## Climate:Source 1 74.82 0.7545 0.38507
## Residuals 28 2362.83
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 5, p-value = 0.2353
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -68.34270 13.97676
## sample estimates:
## difference in location
## -27.18296
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 16, p-value = 0.5228
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -10.44161 67.94132
## sample estimates:
## difference in location
## 29.64229
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 26, p-value = 0.279
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.354092 4.988486
## sample estimates:
## difference in location
## 2.066666
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 28, p-value = 0.1703
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.9948177 10.0975854
## sample estimates:
## difference in location
## 5.62787
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 22, p-value = 0.08284
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.8221862 15.0872946
## sample estimates:
## difference in location
## 7.651679
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 51, p-value = 0.2366
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -6.951583 1.649500
## sample estimates:
## difference in location
## -3.570295
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 10, p-value = 0.279
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -5.569969 2.833986
## sample estimates:
## difference in location
## -1.641379
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 15, p-value = 0.7182
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -7.943049 5.141081
## sample estimates:
## difference in location
## -2.618944
##
## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Phylum
## OTU7 "Cyanobacteria"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 95.56 0.9636 0.32628
## Source 3 853.37 8.6055 0.03502
## Climate:Source 1 45.07 0.4545 0.50023
## Residuals 28 2278.50
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 19, p-value = 0.2353
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -2.455407 18.180308
## sample estimates:
## difference in location
## 2.957942
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 6, p-value = 0.3153
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -37.738611 2.344579
## sample estimates:
## difference in location
## -3.324833
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 33, p-value = 0.03636
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.6713462 18.0874183
## sample estimates:
## difference in location
## 3.080276
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 34, p-value = 0.02527
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.06861458 24.43779425
## sample estimates:
## difference in location
## 5.272126
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 20, p-value = 0.1709
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.5906178 37.8005207
## sample estimates:
## difference in location
## 4.208215
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 73, p-value = 0.977
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -7.311598 5.103880
## sample estimates:
## difference in location
## 0.2627632
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 21, p-value = 0.7182
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -3.692326 14.614652
## sample estimates:
## difference in location
## 1.12547
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 16, p-value = 0.8286
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -23.702686 4.781333
## sample estimates:
## difference in location
## -1.638922
##
## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Phylum
## OTU10 "Acidobacteria"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 2.38 0.0240 0.87682
## Source 3 1299.20 13.1012 0.00442
## Climate:Source 1 70.42 0.7101 0.39942
## Residuals 28 1900.50
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 10, p-value = 0.7842
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.875998 6.843540
## sample estimates:
## difference in location
## -0.2000774
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 5, p-value = 0.2353
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -3.891498 1.987482
## sample estimates:
## difference in location
## -1.607997
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 18, p-value = 1
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.440454 1.139749
## sample estimates:
## difference in location
## -0.007451021
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 28, p-value = 0.1703
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.8000388 2.8098831
## sample estimates:
## difference in location
## 1.66951
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 21, p-value = 0.1207
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.8738157 4.2049748
## sample estimates:
## difference in location
## 2.407387
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 40, p-value = 0.06896
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -2.5382471 0.1114779
## sample estimates:
## difference in location
## -1.540419
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -10.418316 -7.394329
## sample estimates:
## difference in location
## -8.59669
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 36, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 5.710241 9.481740
## sample estimates:
## difference in location
## 7.038468
##
## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Phylum
## OTU20 "Actinobacteria"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 584.74 5.8965 0.01517
## Source 3 761.45 7.6785 0.05315
## Climate:Source 1 0.82 0.0082 0.92769
## Residuals 28 1925.50
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 22, p-value = 0.08284
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -10.12738 56.93452
## sample estimates:
## difference in location
## 36.90999
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 7, p-value = 0.4113
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -44.932741 9.863263
## sample estimates:
## difference in location
## -6.636044
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 29, p-value = 0.1296
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -6.174561 41.738766
## sample estimates:
## difference in location
## 20.55627
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 14, p-value = 0.6134
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -23.05721 22.19200
## sample estimates:
## difference in location
## -6.063935
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 23, p-value = 0.05523
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.954275 46.692652
## sample estimates:
## difference in location
## 12.84274
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 115, p-value = 0.01414
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 7.16780 40.31334
## sample estimates:
## difference in location
## 21.9016
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 27, p-value = 0.2199
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -11.03421 33.22758
## sample estimates:
## difference in location
## 17.11401
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 25, p-value = 0.3481
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -19.10688 25.75876
## sample estimates:
## difference in location
## 15.87242
##
## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Phylum
## OTU44 "Firmicutes"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 362.38 3.6543 0.05593
## Source 3 1569.80 15.8299 0.00123
## Climate:Source 1 1.07 0.0108 0.91740
## Residuals 28 1339.25
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.03576
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -8.618072 -4.812207
## sample estimates:
## difference in location
## -6.671831
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 24, p-value = 0.03576
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.2551915 8.7211006
## sample estimates:
## difference in location
## 5.343429
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 2, p-value = 0.02527
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.01263008 -0.03357744
## sample estimates:
## difference in location
## -0.3462271
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 23, p-value = 0.516
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.6665798 2.1292269
## sample estimates:
## difference in location
## 0.1278182
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 8, p-value = 0.5228
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -2.274515 4.521171
## sample estimates:
## difference in location
## -0.2052955
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 20, p-value = 0.002946
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.6614044 -0.2036041
## sample estimates:
## difference in location
## -0.5325774
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.2671454 -0.7882357
## sample estimates:
## difference in location
## -0.9980437
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 22, p-value = 0.6134
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.505982 1.119910
## sample estimates:
## difference in location
## 0.5223647
##
## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Phylum
## OTU116 "Gemmatimonadetes"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 536.03 5.4053 0.02008
## Source 3 1335.07 13.4629 0.00374
## Climate:Source 1 29.40 0.2965 0.58610
## Residuals 28 1372.00
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 13, p-value = 0.9273
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.563363 2.368097
## sample estimates:
## difference in location
## 0.1096169
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 14, p-value = 0.7842
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.5939111 1.5794065
## sample estimates:
## difference in location
## 0.4927523
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 5, p-value = 0.0712
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -2.6300118 0.1465505
## sample estimates:
## difference in location
## -1.280392
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -2.8652168 -0.9657726
## sample estimates:
## difference in location
## -1.923801
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 21, p-value = 0.1207
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.1146488 0.6986154
## sample estimates:
## difference in location
## 0.2262944
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 115, p-value = 0.01414
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.06797809 1.12543652
## sample estimates:
## difference in location
## 0.575367
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -5.196120 -3.074545
## sample estimates:
## difference in location
## -4.256295
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 36, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 4.371457 5.332202
## sample estimates:
## difference in location
## 4.958494
##
## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Phylum
## OTU225 "Chloroflexi"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 724.97 7.3106 0.00685
## Source 3 1423.13 14.3509 0.00246
## Climate:Source 1 17.07 0.1721 0.67825
## Residuals 28 1107.33
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 22, p-value = 0.08284
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.888273 27.556451
## sample estimates:
## difference in location
## 8.68754
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 13, p-value = 0.9273
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -2.388231 2.129751
## sample estimates:
## difference in location
## 0.2617992
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 6, p-value = 0.09694
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -19.842294 5.726156
## sample estimates:
## difference in location
## -8.397385
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -23.17677 -10.76493
## sample estimates:
## difference in location
## -21.05372
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 14, p-value = 0.7842
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.822953 2.765635
## sample estimates:
## difference in location
## 0.1344354
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 133, p-value = 0.0004777
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 4.219769 15.572571
## sample estimates:
## difference in location
## 8.960243
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 10, p-value = 0.279
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -15.090981 6.626764
## sample estimates:
## difference in location
## -6.713824
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 36, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 13.68642 17.40189
## sample estimates:
## difference in location
## 16.2042Taxa_tests_order <- STAMPR(Rock_weathering_filt3_GMPR, "Order", sig_pairs)## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Order
## OTU1 "Burkholderiales"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 900.74 9.0830 0.00258
## Source 3 1292.08 13.0294 0.00457
## Climate:Source 1 79.35 0.8002 0.37104
## Residuals 28 1000.33
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.03576
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -41.194215 -3.356289
## sample estimates:
## difference in location
## -22.27526
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 12, p-value = 1
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -40.18183 41.15491
## sample estimates:
## difference in location
## -0.3563909
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 2, p-value = 0.02527
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.4548998 -0.1261025
## sample estimates:
## difference in location
## -0.2969459
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 33, p-value = 0.03636
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 12.65335 40.77171
## sample estimates:
## difference in location
## 34.15781
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 22, p-value = 0.08284
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.325322 43.909135
## sample estimates:
## difference in location
## 34.7954
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 9, p-value = 0.000308
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -36.54463 -15.27047
## sample estimates:
## difference in location
## -34.67256
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.1343499 -0.3934692
## sample estimates:
## difference in location
## -0.74279
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 3, p-value = 0.03636
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -40.52361 -11.97382
## sample estimates:
## difference in location
## -33.78455
##
## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Order
## OTU5 "Sphingobacteriales"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 183.56 1.8510 0.17367
## Source 3 1245.17 12.5564 0.00570
## Climate:Source 1 11.27 0.1136 0.73607
## Residuals 28 1832.50
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 8, p-value = 0.5228
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.1104079 0.8423098
## sample estimates:
## difference in location
## -0.3089557
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 5, p-value = 0.2353
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -13.082829 1.131093
## sample estimates:
## difference in location
## -6.123185
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 21, p-value = 0.7182
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.6640484 0.7639180
## sample estimates:
## difference in location
## 0.01919754
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 30, p-value = 0.09694
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.5096519 10.2461215
## sample estimates:
## difference in location
## 6.491376
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 21, p-value = 0.1207
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.4782018 13.6953205
## sample estimates:
## difference in location
## 6.46166
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 23, p-value = 0.005108
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -8.5172328 -0.8528489
## sample estimates:
## difference in location
## -6.135539
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -4.329414 -1.612209
## sample estimates:
## difference in location
## -3.002078
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 14, p-value = 0.6134
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -8.039024 3.572766
## sample estimates:
## difference in location
## -3.718278
##
## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Order
## OTU6 "Enterobacteriales"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 70.62 0.7121 0.39874
## Source 3 2314.23 23.3368 0.00003
## Climate:Source 1 64.07 0.6461 0.42153
## Residuals 28 823.58
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 20, p-value = 0.1709
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.5713626 91.1323680
## sample estimates:
## difference in location
## 3.553661
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 22, p-value = 0.08284
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -10.437379 1.912076
## sample estimates:
## difference in location
## 1.69055
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 3, p-value = 0.03636
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -49.501489 -5.472717
## sample estimates:
## difference in location
## -21.46237
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -51.68982 -22.42206
## sample estimates:
## difference in location
## -26.58309
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.03576
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -84.18527 -55.63994
## sample estimates:
## difference in location
## -69.91261
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 134, p-value = 0.0003842
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 1.932528 8.058776
## sample estimates:
## difference in location
## 5.11499
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 36, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 1.298489 17.365156
## sample estimates:
## difference in location
## 5.334665
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 8, p-value = 0.1703
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.58435850 0.06542455
## sample estimates:
## difference in location
## -0.1028499
##
## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Order
## OTU7 "SubsectionII"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 174.38 1.7585 0.18481
## Source 3 716.55 7.2257 0.06504
## Climate:Source 1 2.40 0.0242 0.87637
## Residuals 28 2379.17
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 14, p-value = 0.7842
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.04641 14.86629
## sample estimates:
## difference in location
## 0.1945796
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 4, p-value = 0.1709
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -33.5566632 0.8541497
## sample estimates:
## difference in location
## -3.536299
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 22, p-value = 0.6134
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.2696644 9.1526642
## sample estimates:
## difference in location
## 0.5183234
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 34, p-value = 0.02527
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.1576628 23.9114605
## sample estimates:
## difference in location
## 4.181034
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 21, p-value = 0.1207
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.3651775 33.6630338
## sample estimates:
## difference in location
## 4.549962
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 41, p-value = 0.07825
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -8.940779 1.057636
## sample estimates:
## difference in location
## -1.222737
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 20, p-value = 0.8286
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.5775934 9.0335277
## sample estimates:
## difference in location
## 0.2127829
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 5, p-value = 0.0712
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -23.7924121 0.1227166
## sample estimates:
## difference in location
## -3.891536
##
## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Order
## OTU9 "Rhizobiales"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 880.26 8.8766 0.002888
## Source 3 546.82 5.5141 0.137795
## Climate:Source 1 400.42 4.0378 0.044491
## Residuals 28 1445.00
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 9, p-value = 0.6481
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -5.881060 2.947064
## sample estimates:
## difference in location
## -1.466998
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 6, p-value = 0.3153
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -11.662705 5.901289
## sample estimates:
## difference in location
## -3.357776
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 16, p-value = 0.8286
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.990814 1.211501
## sample estimates:
## difference in location
## -0.5011302
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 33, p-value = 0.03636
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.9757943 10.1285233
## sample estimates:
## difference in location
## 4.438603
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 8, p-value = 0.5228
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -7.848487 4.603742
## sample estimates:
## difference in location
## -1.200014
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 12, p-value = 0.000592
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -7.323014 -2.448342
## sample estimates:
## difference in location
## -4.6125
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -4.588206 -2.234018
## sample estimates:
## difference in location
## -2.956213
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 12, p-value = 0.4273
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -7.265095 1.276736
## sample estimates:
## difference in location
## -1.607365
##
## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Order
## OTU10 "Subgroup_4"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 49.44 0.4986 0.48013
## Source 3 1314.88 13.2592 0.00411
## Climate:Source 1 72.60 0.7321 0.39220
## Residuals 28 1835.58
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 10, p-value = 0.7842
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.355819 6.528750
## sample estimates:
## difference in location
## -0.1126584
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 4, p-value = 0.1709
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -4.075922 1.321435
## sample estimates:
## difference in location
## -1.911203
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 14, p-value = 0.6134
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.9403130 0.8227086
## sample estimates:
## difference in location
## -0.265193
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 30, p-value = 0.09694
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.5559751 3.2464937
## sample estimates:
## difference in location
## 1.832547
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 22, p-value = 0.08284
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.6122869 4.2773329
## sample estimates:
## difference in location
## 2.400129
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 31, p-value = 0.01937
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -2.8472840 -0.3238103
## sample estimates:
## difference in location
## -1.969475
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 3, p-value = 0.03636
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -6.169976 -4.029602
## sample estimates:
## difference in location
## -5.295554
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 36, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 1.716829 5.439081
## sample estimates:
## difference in location
## 3.039978
##
## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Order
## OTU11 "Sphingomonadales"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 4.97 0.0501 0.82285
## Source 3 595.88 6.0089 0.11118
## Climate:Source 1 66.15 0.6671 0.41408
## Residuals 28 2605.50
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 13, p-value = 0.9273
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -5.903441 12.844720
## sample estimates:
## difference in location
## 0.13997
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 9, p-value = 0.6481
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -8.326224 4.103880
## sample estimates:
## difference in location
## -0.7099819
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 20, p-value = 0.8286
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -4.128869 5.956528
## sample estimates:
## difference in location
## 0.3258721
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 22, p-value = 0.6134
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -3.226862 5.505896
## sample estimates:
## difference in location
## 1.482761
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 24, p-value = 0.03576
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 1.378665 9.581825
## sample estimates:
## difference in location
## 5.117695
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 52, p-value = 0.2602
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -3.503731 1.094646
## sample estimates:
## difference in location
## -1.199836
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 12, p-value = 0.4273
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -3.995816 3.069399
## sample estimates:
## difference in location
## -1.0588
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 15, p-value = 0.7182
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -2.890330 2.811984
## sample estimates:
## difference in location
## -0.5360087
##
## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Order
## OTU14 "Caulobacterales"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 430.62 4.3424 0.03718
## Source 3 1247.53 12.5802 0.00564
## Climate:Source 1 40.02 0.4035 0.52527
## Residuals 28 1554.33
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 9, p-value = 0.6481
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.0704490 0.9475039
## sample estimates:
## difference in location
## -0.03852294
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 5, p-value = 0.2353
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -2.9373695 0.5232694
## sample estimates:
## difference in location
## -0.8493462
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 24, p-value = 0.4273
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.4313298 0.7733929
## sample estimates:
## difference in location
## 0.218237
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 35, p-value = 0.01724
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.2813935 2.4611436
## sample estimates:
## difference in location
## 1.096272
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 24, p-value = 0.03576
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.3652594 3.0358987
## sample estimates:
## difference in location
## 1.288226
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 17, p-value = 0.001652
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.6797914 -0.4360266
## sample estimates:
## difference in location
## -0.9214106
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 18, p-value = 1
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.4322340 0.3639032
## sample estimates:
## difference in location
## 0.01551463
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -2.043705 -0.096489
## sample estimates:
## difference in location
## -0.9607206
##
## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Order
## OTU20 "Rubrobacterales"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 362.38 3.6543 0.05593
## Source 3 1283.60 12.9439 0.00476
## Climate:Source 1 7.35 0.0741 0.78543
## Residuals 28 1619.17
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 24, p-value = 0.03576
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 1.343966 35.280345
## sample estimates:
## difference in location
## 23.05921
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.03576
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -34.39409979 -0.07048124
## sample estimates:
## difference in location
## -4.199694
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 33, p-value = 0.03636
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 5.206833 27.553233
## sample estimates:
## difference in location
## 17.11954
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 19, p-value = 0.9425
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -6.01319 27.58079
## sample estimates:
## difference in location
## 0.3061655
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 23, p-value = 0.05523
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.1729386 34.3585874
## sample estimates:
## difference in location
## 4.639865
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 110, p-value = 0.03038
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 1.007057 21.596004
## sample estimates:
## difference in location
## 13.50276
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 33, p-value = 0.03636
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 5.377442 24.819725
## sample estimates:
## difference in location
## 16.82808
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 20, p-value = 0.8286
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -26.414743 5.855238
## sample estimates:
## difference in location
## 1.69516
##
## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Order
## OTU22 "Cytophagales"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 1164.74 11.7452 0.00061
## Source 3 44.85 0.4522 0.92925
## Climate:Source 1 281.67 2.8403 0.09192
## Residuals 28 1781.25
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 7, p-value = 0.4113
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -67.49137 13.79442
## sample estimates:
## difference in location
## -27.69087
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 24, p-value = 0.03576
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.9995852 67.2950902
## sample estimates:
## difference in location
## 34.90214
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 26, p-value = 0.279
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.069587 5.465177
## sample estimates:
## difference in location
## 1.337481
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 10, p-value = 0.279
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.8392488 0.3526159
## sample estimates:
## difference in location
## -0.2786225
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 23, p-value = 0.05523
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.1948212 1.8870346
## sample estimates:
## difference in location
## 0.5415788
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 115, p-value = 0.01414
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.416069 3.919566
## sample estimates:
## difference in location
## 2.374847
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 23, p-value = 0.516
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.866692 5.145857
## sample estimates:
## difference in location
## 1.171801
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 34, p-value = 0.02527
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.1545652 1.8629482
## sample estimates:
## difference in location
## 1.157305
##
## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Order
## OTU30 "Micrococcales"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 265.44 2.6767 0.10183
## Source 3 749.89 7.5619 0.05599
## Climate:Source 1 33.75 0.3403 0.55964
## Residuals 28 2223.42
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 2, p-value = 0.08284
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -5.206538 4.628937
## sample estimates:
## difference in location
## -2.182672
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 22, p-value = 0.08284
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -3.828908 5.114544
## sample estimates:
## difference in location
## 2.137151
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 25, p-value = 0.3481
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.5320493 1.4765078
## sample estimates:
## difference in location
## 0.2706085
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 26, p-value = 0.279
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.5122759 0.7866787
## sample estimates:
## difference in location
## 0.2027858
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 7, p-value = 0.4113
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.5000857 5.5661994
## sample estimates:
## difference in location
## -0.2281722
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 79, p-value = 0.7075
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.2204756 0.3690566
## sample estimates:
## difference in location
## 0.0729464
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 4, p-value = 0.05135
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.5258004 0.2675506
## sample estimates:
## difference in location
## -1.065072
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 33, p-value = 0.03636
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.4223112 1.4948001
## sample estimates:
## difference in location
## 1.103329
##
## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Order
## OTU33 "Frankiales"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 64.97 0.65517 0.41827
## Source 3 262.68 2.64887 0.44899
## Climate:Source 1 9.60 0.09681 0.75570
## Residuals 28 2935.25
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 16, p-value = 0.5228
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -2.724773 10.232076
## sample estimates:
## difference in location
## 1.105793
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 8, p-value = 0.5228
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -6.780411 2.832335
## sample estimates:
## difference in location
## -0.4845265
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 19, p-value = 0.9425
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.568094 4.262904
## sample estimates:
## difference in location
## 0.2980444
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 16, p-value = 0.8286
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -2.577901 4.274808
## sample estimates:
## difference in location
## -0.1588814
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 18, p-value = 0.3153
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.7863939 6.5607849
## sample estimates:
## difference in location
## 1.092154
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 82, p-value = 0.5834
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.373038 1.650491
## sample estimates:
## difference in location
## 0.5690406
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 17, p-value = 0.9425
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.015699 2.983868
## sample estimates:
## difference in location
## -0.1357949
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 21, p-value = 0.7182
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -4.105227 1.803981
## sample estimates:
## difference in location
## 0.4714685
##
## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Order
## OTU40 "Deinococcales"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 24.74 0.2494 0.61748
## Source 3 1445.20 14.5734 0.00222
## Climate:Source 1 190.82 1.9242 0.16539
## Residuals 28 1611.75
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 24, p-value = 0.03576
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.1159525 28.8971507
## sample estimates:
## difference in location
## 3.763569
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.03576
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -7.3472144 -0.5879807
## sample estimates:
## difference in location
## -2.500071
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 18, p-value = 1
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -8.04070 20.81984
## sample estimates:
## difference in location
## 0.1319845
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 11, p-value = 0.3481
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -7.129548 2.026183
## sample estimates:
## difference in location
## -2.583399
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 24, p-value = 0.03576
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.5144736 7.4691341
## sample estimates:
## difference in location
## 2.253139
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 82, p-value = 0.5834
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.344459 18.286424
## sample estimates:
## difference in location
## 0.9522018
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 36, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.3237722 25.0161232
## sample estimates:
## difference in location
## 2.685594
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -4.2047144 -0.6847059
## sample estimates:
## difference in location
## -2.525927
##
## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Order
## OTU73 "Solirubrobacterales"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 670.62 6.7625 0.00931
## Source 3 1220.47 12.3072 0.00640
## Climate:Source 1 3.75 0.0378 0.84581
## Residuals 28 1377.67
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 22, p-value = 0.08284
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.2353533 27.6409333
## sample estimates:
## difference in location
## 4.612546
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 5, p-value = 0.2353
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -2.9351537 0.2608993
## sample estimates:
## difference in location
## -0.4958395
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 18, p-value = 1
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -4.212403 9.958079
## sample estimates:
## difference in location
## -0.1090627
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -5.293649 -2.537352
## sample estimates:
## difference in location
## -4.224644
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 21, p-value = 0.1207
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.1764773 3.0664060
## sample estimates:
## difference in location
## 0.5754338
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 125, p-value = 0.002437
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 1.196491 11.780432
## sample estimates:
## difference in location
## 3.693183
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 17, p-value = 0.9425
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -4.307577 10.065980
## sample estimates:
## difference in location
## -0.4198441
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 36, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 2.036055 5.953411
## sample estimates:
## difference in location
## 3.962095
##
## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Order
## OTU144 "Acidimicrobiales"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 402.62 4.0600 0.04391
## Source 3 1958.32 19.7477 0.00019
## Climate:Source 1 36.82 0.3713 0.54232
## Residuals 28 874.75
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 22, p-value = 0.08284
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.2770006 1.8753928
## sample estimates:
## difference in location
## 0.9466836
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 8, p-value = 0.5228
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.6952939 0.2939760
## sample estimates:
## difference in location
## -0.07693071
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -8.055194 -2.733487
## sample estimates:
## difference in location
## -4.376145
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -8.811553 -3.722903
## sample estimates:
## difference in location
## -5.306655
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 23, p-value = 0.05523
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.009727731 0.707286393
## sample estimates:
## difference in location
## 0.2041089
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 125, p-value = 0.002437
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.3522632 1.1649116
## sample estimates:
## difference in location
## 0.8707461
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -3.351919 -1.703828
## sample estimates:
## difference in location
## -2.347733
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 36, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 2.648734 4.108222
## sample estimates:
## difference in location
## 3.124562
##
## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Order
## OTU225 "JG30-KF-CM45"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 1050.62 10.5945 0.00113
## Source 3 1025.72 10.3434 0.01586
## Climate:Source 1 20.42 0.2059 0.65001
## Residuals 28 1175.75
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 22, p-value = 0.08284
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.3350587 20.3127324
## sample estimates:
## difference in location
## 4.143156
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 14, p-value = 0.7842
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.4073917 0.4038865
## sample estimates:
## difference in location
## 0.007441521
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 19, p-value = 0.9425
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -3.843441 11.810661
## sample estimates:
## difference in location
## 0.6013048
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -4.795687 -2.220769
## sample estimates:
## difference in location
## -4.477859
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 18, p-value = 0.3153
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.1554704 0.5143634
## sample estimates:
## difference in location
## 0.1156305
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 135, p-value = 0.000308
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 2.579639 6.320156
## sample estimates:
## difference in location
## 4.241255
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 17, p-value = 0.9425
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -3.807166 10.334104
## sample estimates:
## difference in location
## -0.2087371
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 36, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 3.697211 4.574490
## sample estimates:
## difference in location
## 4.299239Ternary plots
For the arid samples
Rock_weathering_filt3_GMPR_Arid_rel <- transform_sample_counts(Rock_weathering_filt3_GMPR_Arid, function(x) x / sum(x) ) # rel abundance
Rock_weathering_filt3_GMPR_Arid_merged <- merge_samples(Rock_weathering_filt3_GMPR_Arid_rel, "Source", fun = mean) # merge by source
Rock_weathering_filt3_GMPR_Arid_merged_rel <- transform_sample_counts(Rock_weathering_filt3_GMPR_Arid_merged, function(x) x / sum(x) ) # rel abundance per source
meandf <- as(otu_table(Rock_weathering_filt3_GMPR_Arid_merged_rel), "matrix")
if (!taxa_are_rows(Rock_weathering_filt3_GMPR_Arid_merged_rel)) { meandf <- t(meandf) }
abundance <- rowSums(meandf) / sum(meandf) * 100
Arid4Ternary <- data.frame(
meandf,
Abundance = abundance,
Phylum = tax_table(Rock_weathering_filt3_GMPR_Arid_merged_rel)[, "Phylum"]
)
# Arid4Ternary <- dplyr::rename(Arid4Ternary, Loess_soil = Loess.soil)
Arid4Ternary$Phylum <-
factor(Arid4Ternary$Phylum, levels = c(levels(Arid4Ternary$Phylum), 'Rare'))
Arid4Ternary$Phylum[Arid4Ternary$Phylum %in% Rare_phyla] <- "Rare"
Arid4Ternary$Phylum %<>%
factor(., levels = Taxa_rank$Phylum) %>%
fct_relevel(., "Rare", after = Inf)
p_ternary_arid <-
ggtern(data = Arid4Ternary,
aes(
x = Loess.soil,
y = Dust,
z = Limestone,
size = Abundance,
colour = Phylum
)) +
geom_point(alpha = 1 / 2) +
scale_size(
range = c(1, 5),
name = "Abundance (%)"
) +
theme_arrownormal() +
scale_color_manual(values = pal("d3js")) +
guides(colour = guide_legend(override.aes = list(size = 3))) +
labs(x = "Loess soil") +
theme(axis.title = element_blank())
print(p_ternary_arid)
For the hyperarid samples
Rock_weathering_filt3_GMPR_Hyperarid_rel <- transform_sample_counts(Rock_weathering_filt3_GMPR_Hyperarid, function(x) x / sum(x) ) # rel abundance
Rock_weathering_filt3_GMPR_Hyperarid_merged <- merge_samples(Rock_weathering_filt3_GMPR_Hyperarid_rel, "Source", fun = mean) # merge by source
Rock_weathering_filt3_GMPR_Hyperarid_merged_rel <- transform_sample_counts(Rock_weathering_filt3_GMPR_Hyperarid_merged, function(x) x / sum(x) ) # rel abundance per source
meandf <- as(otu_table(Rock_weathering_filt3_GMPR_Hyperarid_merged_rel), "matrix")
if (!taxa_are_rows(Rock_weathering_filt3_GMPR_Hyperarid_merged_rel)) { meandf <- t(meandf) }
abundance <- rowSums(meandf) / sum(meandf) * 100
Hyperarid4Ternary <- data.frame(
meandf,
Abundance = abundance,
Phylum = tax_table(Rock_weathering_filt3_GMPR_Hyperarid_merged_rel)[, "Phylum"]
)
Hyperarid4Ternary$Phylum <-
factor(Hyperarid4Ternary$Phylum, levels = c(levels(Hyperarid4Ternary$Phylum), 'Rare'))
Hyperarid4Ternary$Phylum[Hyperarid4Ternary$Phylum %in% Rare_phyla] <- "Rare"
Hyperarid4Ternary$Phylum %<>%
factor(., levels = Taxa_rank$Phylum) %>%
fct_relevel(., "Rare", after = Inf)
p_ternary_hyperarid <-
ggtern(data = Hyperarid4Ternary,
aes(
x = Loess.soil,
y = Dust,
z = Dolomite,
size = Abundance,
colour = Phylum
)) +
geom_point(alpha = 1 / 2) +
scale_size(
range = c(1, 5),
name = "Abundance (%)"
) +
theme_arrownormal() +
scale_color_manual(values = pal("d3js")) +
guides(colour = guide_legend(override.aes = list(size = 3))) +
labs(x = "Loess soil") +
theme(axis.title = element_blank())
print(p_ternary_hyperarid)
Combine plots
Combine all sequence analysis plots to make Fig. 3
ternary_legend <-
get_legend(p_ternary_arid)# + theme(legend.direction = "horizontal"))
ord_legend <- get_legend(p_ord)
top_row <-
plot_grid(
p_alpha + theme(
legend.position = "none",
panel.spacing = unit(0.5, "lines")
),
p_ord + theme(axis.title.y = element_text(vjust = -3)) ,
labels = c('A', 'B'),
label_size = 12,
align = 'v',
axis = "tl",
nrow = 1,
ncol = 2
)
bottom_l <-
plot_grid(
p_taxa_box + theme(legend.position = "none"),
labels = c('C'),
label_size = 12,
ncol = 1
)
bottom_r <-
plot_grid(
p_ternary_arid +
theme(legend.position = "none",
plot.margin = unit(c(-0.1, -0.1, -0.1, -0.1), "cm"),
axis.title = element_blank()),
p_ternary_hyperarid +
theme(legend.position = "none",
plot.margin = unit(c(-0.1, -0.1, -0.1, -0.1), "cm"),
axis.title = element_blank()),
labels = c('D'),
label_size = 12,
align = 'hv',
axis = "t",
# rel_widths = c(1, 1, 0.1),
scale = c(1.2, 1.2),
nrow = 2,
ncol = 1
)
bottom_rows <- plot_grid(bottom_l,
bottom_r,
ternary_legend,
align = 'h',
axis = "l",
scale = c(1, 1, 0.08),
rel_widths = c(0.5, 0.35, 0.15),
nrow = 1,
ncol = 3)
p_all <- plot_grid(top_row, bottom_rows, align = 'v', axis = 'l', nrow = 2, rel_heights = c(0.43, 0.6)) # aligning vertically along the left axis
print(p_all)
Differential abundance models
Detect differentially abundant OTUs using ALDEx2 (Fernandes et al. 02AD–2013)
# Rock_weathering_filt3_s <- prune_taxa(names(sort(taxa_sums(Rock_weathering_filt3), TRUE))[1:100], Rock_weathering_filt3)
# run full model
data2test <- t(otu_table(Rock_weathering_filt3))
comparison <- as.character(unlist(sample_data(Rock_weathering_filt3)[, "Climate.Source"]))
ALDEx_full <- aldex.clr(data2test, comparison, mc.samples = 128, denom = "iqlr", verbose = TRUE, useMC = TRUE) # iqlr for slight assymetry in composition## [1] "multicore environment is is OK -- using the BiocParallel package"
## [1] "removed rows with sums equal to zero"
## [1] "computing iqlr centering"
## [1] "data format is OK"
## [1] "dirichlet samples complete"
## [1] "clr transformation complete"ALDEx_full_glm <- aldex.glm(ALDEx_full, comparison, useMC = TRUE) # for more than two conditions## [1] "multicore environment is OK -- using the BiocParallel package"
## [1] "running tests for each MC instance:"
## |------------(25%)----------(50%)----------(75%)----------|sig_taxa <- rownames(ALDEx_full_glm)[ALDEx_full_glm$glm.eBH < 0.05] # save names of taxa that are significant under the full model
# Pairwise comparisons
#
# dolomite - limestone
Rock_weathering_filt3_Rocks <- subset_samples(Rock_weathering_filt3, Uni.Source == "Rock")
ALDEx2plot_Rocks <- CalcALDEx(Rock_weathering_filt3_Rocks, sig_level = 0.1, LFC = 0)## [1] "multicore environment is is OK -- using the BiocParallel package"
## [1] "removed rows with sums equal to zero"
## [1] "computing iqlr centering"
## [1] "data format is OK"
## [1] "dirichlet samples complete"
## [1] "clr transformation complete"
## [1] "running tests for each MC instance:"
## |------------(25%)----------(50%)----------(75%)----------|
## [1] "multicore environment is OK -- using the BiocParallel package"
## [1] "sanity check complete"
## [1] "rab.all complete"
## [1] "rab.win complete"
## [1] "rab of samples complete"
## [1] "within sample difference calculated"
## [1] "between group difference calculated"
## [1] "group summaries calculated"
## [1] "effect size calculated"
## [1] "summarizing output"GGPlotALDExTax(ALDEx2plot_Rocks) +
ggtitle("Hyperarid dolomite vs. Arid limestone")
# dolomite - soil
Rock_weathering_filt3_DolSoil <- subset_samples(Rock_weathering_filt3, Climate == "Hyperarid" & Source != "Dust")
ALDEx2plot_DolSoil <- CalcALDEx(Rock_weathering_filt3_DolSoil, sig_level = 0.1, LFC = 0)## [1] "multicore environment is is OK -- using the BiocParallel package"
## [1] "removed rows with sums equal to zero"
## [1] "computing iqlr centering"
## [1] "data format is OK"
## [1] "dirichlet samples complete"
## [1] "clr transformation complete"
## [1] "running tests for each MC instance:"
## |------------(25%)----------(50%)----------(75%)----------|
## [1] "multicore environment is OK -- using the BiocParallel package"
## [1] "sanity check complete"
## [1] "rab.all complete"
## [1] "rab.win complete"
## [1] "rab of samples complete"
## [1] "within sample difference calculated"
## [1] "between group difference calculated"
## [1] "group summaries calculated"
## [1] "effect size calculated"
## [1] "summarizing output"GGPlotALDExTax(ALDEx2plot_DolSoil) +
ggtitle("Hyperarid dolomite vs. Hyperarid soil")
# dolomite - dust
Rock_weathering_filt3_DolDust <- subset_samples(Rock_weathering_filt3, Climate == "Hyperarid" & Source != "Loess soil")
ALDEx2plot_DolDust <- CalcALDEx(Rock_weathering_filt3_DolDust, sig_level = 0.3, LFC = 0)## [1] "multicore environment is is OK -- using the BiocParallel package"
## [1] "removed rows with sums equal to zero"
## [1] "computing iqlr centering"
## [1] "data format is OK"
## [1] "dirichlet samples complete"
## [1] "clr transformation complete"
## [1] "running tests for each MC instance:"
## |------------(25%)----------(50%)----------(75%)----------|
## [1] "multicore environment is OK -- using the BiocParallel package"
## [1] "sanity check complete"
## [1] "rab.all complete"
## [1] "rab.win complete"
## [1] "rab of samples complete"
## [1] "within sample difference calculated"
## [1] "between group difference calculated"
## [1] "group summaries calculated"
## [1] "effect size calculated"
## [1] "summarizing output"GGPlotALDExTax(ALDEx2plot_DolDust) +
ggtitle("Hyperarid dolomite vs. Hyperarid dust")
# limestone - soil
Rock_weathering_filt3_LimeSoil <- subset_samples(Rock_weathering_filt3, Climate == "Arid" & Source != "Dust")
ALDEx2plot_LimeSoil <- CalcALDEx(Rock_weathering_filt3_LimeSoil, sig_level = 0.1, LFC = 0)## [1] "multicore environment is is OK -- using the BiocParallel package"
## [1] "removed rows with sums equal to zero"
## [1] "computing iqlr centering"
## [1] "data format is OK"
## [1] "dirichlet samples complete"
## [1] "clr transformation complete"
## [1] "running tests for each MC instance:"
## |------------(25%)----------(50%)----------(75%)----------|
## [1] "multicore environment is OK -- using the BiocParallel package"
## [1] "sanity check complete"
## [1] "rab.all complete"
## [1] "rab.win complete"
## [1] "rab of samples complete"
## [1] "within sample difference calculated"
## [1] "between group difference calculated"
## [1] "group summaries calculated"
## [1] "effect size calculated"
## [1] "summarizing output"GGPlotALDExTax(ALDEx2plot_LimeSoil) +
ggtitle("Arid limestone vs. Arid soil")
# limestone - dust
Rock_weathering_filt3_LimeDust <- subset_samples(Rock_weathering_filt3, Climate == "Arid" & Source != "Loess soil")
ALDEx2plot_LimeDust <- CalcALDEx(Rock_weathering_filt3_LimeDust, sig_level = 0.3, LFC = 0)## [1] "multicore environment is is OK -- using the BiocParallel package"
## [1] "removed rows with sums equal to zero"
## [1] "computing iqlr centering"
## [1] "data format is OK"
## [1] "dirichlet samples complete"
## [1] "clr transformation complete"
## [1] "running tests for each MC instance:"
## |------------(25%)----------(50%)----------(75%)----------|
## [1] "multicore environment is OK -- using the BiocParallel package"
## [1] "sanity check complete"
## [1] "rab.all complete"
## [1] "rab.win complete"
## [1] "rab of samples complete"
## [1] "within sample difference calculated"
## [1] "between group difference calculated"
## [1] "group summaries calculated"
## [1] "effect size calculated"
## [1] "summarizing output"GGPlotALDExTax(ALDEx2plot_LimeDust) +
ggtitle("Arid limestone vs. Arid dust")
ALDEx2plot_Rocks %<>% cbind(., Var1 = "Dolomite", Var2 = "Limestone")
ALDEx2plot_DolSoil %<>% cbind(., Var1 = "Dolomite", Var2 = "Loess soil")
ALDEx2plot_DolDust %<>% cbind(., Var1 = "Dolomite", Var2 = "Dust")
ALDEx2plot_LimeSoil %<>% cbind(., Var1 = "Limestone", Var2 = "Loess soil")
ALDEx2plot_LimeDust %<>% cbind(., Var1 = "Limestone", Var2 = "Dust")
ALDEx2plot_all <- bind_rows(ALDEx2plot_Rocks, ALDEx2plot_DolSoil, ALDEx2plot_DolDust, ALDEx2plot_LimeSoil, ALDEx2plot_LimeDust)
ALDEx2plot_all$Var2 %<>%
factor() %>% # Taxa_rank is calcuted for the taxa box plots
fct_relevel(., "Limestone")
# paste0(percent(sum(ALDEx2plot_Rocks$effect > 0 & ALDEx2plot_Rocks$Significance == "Pass")/nrow(ALDEx2plot_Rocks)), "/", percent(sum(ALDEx2plot_Rocks$effect < 0 & ALDEx2plot_Rocks$Significance == "Pass")/nrow(ALDEx2plot_Rocks)))
Labels <- c(
paste0("⬆", sum(ALDEx2plot_Rocks$effect > 0 & ALDEx2plot_Rocks$Significance == "Pass"), " ⬇", sum(ALDEx2plot_Rocks$effect < 0 & ALDEx2plot_Rocks$Significance == "Pass"), " (", nrow(ALDEx2plot_Rocks), ")"),
paste0("⬆", sum(ALDEx2plot_DolSoil$effect > 0 & ALDEx2plot_DolSoil$Significance == "Pass"), " ⬇", sum(ALDEx2plot_DolSoil$effect < 0 & ALDEx2plot_DolSoil$Significance == "Pass"), " (", nrow(ALDEx2plot_DolSoil), ")"),
paste0("⬆", sum(ALDEx2plot_DolDust$effect > 0 & ALDEx2plot_DolDust$Significance == "Pass"), " ⬇", sum(ALDEx2plot_DolDust$effect < 0 & ALDEx2plot_DolDust$Significance == "Pass"), " (", nrow(ALDEx2plot_DolDust), ")"),
paste0("⬆", sum(ALDEx2plot_LimeSoil$effect > 0 & ALDEx2plot_LimeSoil$Significance == "Pass"), " ⬇", sum(ALDEx2plot_LimeSoil$effect < 0 & ALDEx2plot_LimeSoil$Significance == "Pass"), " (", nrow(ALDEx2plot_LimeSoil), ")"),
paste0("⬆", sum(ALDEx2plot_LimeDust$effect > 0 & ALDEx2plot_LimeDust$Significance == "Pass"), " ⬇", sum(ALDEx2plot_LimeDust$effect < 0 & ALDEx2plot_LimeDust$Significance == "Pass"), " (", nrow(ALDEx2plot_LimeDust), ")")
)
Label_text <- bind_cols(
unique(ALDEx2plot_all[c("Var1", "Var2")]),
Label = Labels
)p_aldex2_all <- GGPlotALDExTax(ALDEx2plot_all) +
facet_grid(Var2 ~ Var1, scales = "free_y") +
# theme(strip.background = element_blank(), strip.placement = "outside") +
geom_text(
data = Label_text,
mapping = aes(x = Inf, y = Inf, label = Label),
hjust = 1.1,
vjust = 1.6
)
print(p_aldex2_all)
Other plots
Other plots in the paper which are not based on sequence data ### Isotopes profile
Isotopes <-
read_csv(
"Data/Isotopes_data.csv"
)
Isotopes %<>%
mutate(Mean.Arid = (`Limestone Shivta Fm. NWSH1` + `Limestone Shivta Fm. NWSH2`) / 2)
Isotopes %<>%
mutate(Mean.Hyperarid = (`Dolomite Gerofit Fm.UVSL5` + `Dolomite Gerofit Fm.UVSL6` ) / 2)
Isotopes2plot <- data.frame(
Rock = factor(c(rep("Limestone", 10), rep("Dolomite", 10)),
levels = c("Limestone", "Dolomite")),
Depth = rep(Isotopes$`Depth (mm)`, 2),
Isotope = rep(Isotopes$Isotope, 2),
min = c(
pmin(
Isotopes$`Limestone Shivta Fm. NWSH1`,
Isotopes$`Limestone Shivta Fm. NWSH2`
),
pmin(
Isotopes$`Dolomite Gerofit Fm.UVSL5`,
Isotopes$`Dolomite Gerofit Fm.UVSL6`
)
),
max = c(
pmax(
Isotopes$`Limestone Shivta Fm. NWSH1`,
Isotopes$`Limestone Shivta Fm. NWSH2`
),
pmax(
Isotopes$`Dolomite Gerofit Fm.UVSL5`,
Isotopes$`Dolomite Gerofit Fm.UVSL6`
)
),
mean = c(Isotopes$Mean.Arid, Isotopes$Mean.Hyperarid)
)
p_isotopes <-
ggplot(Isotopes2plot, aes(y = mean, x = Depth, colour = Isotope)) +
geom_point(size = 4, alpha = 1 / 2) +
geom_errorbar(aes(ymin = min, ymax = max), alpha = 1/2, width = 0.2) +
geom_line(alpha = 1 / 2) +
coord_flip() +
theme_cowplot(font_size = 18, font_family = f_name) +
background_grid(major = "xy",
minor = "none") +
scale_x_reverse(limits = c(4.1, -0.1), expand = c(0.01, 0.01)) +
# scale_x_continuous(limits = c(0, 50), expand = c(0.01, 0.01)) +
facet_grid(Rock ~ . , scales = "free_x", labeller = label_parsed) +
scale_color_manual(values = pom4[c(2,1)],
labels = c(expression(paste(delta ^ {13}, "C")),
expression(paste(delta ^ {18}, "O")))) +
ylab(expression(paste(delta ^ {13}, "C / ",
delta ^ {18}, "O", " (", "\u2030", "VPDB",")"
)))
p_isotopes <- plot_grid(p_isotopes, labels = "b", label_size = 20)
print(p_isotopes)
Drying experiment
read_csv("Data/Drying_data_full.csv") ->
Drying_long
Drying_long$Rock %<>% fct_relevel(., "Limestone")
Drying_long$BRC %<>%
fct_relevel(., "Present")
Drying_long$Sample <- with(Drying_long, paste(Rock, BRC))
Drying_mods <- tibble(Sample = character(), Intercept = numeric(), b = numeric(), a = numeric(), P = numeric(), R2 = numeric())
mods <- list()
j <- 1
for (i in unique(Drying_long$Sample)) {
data2model <- Drying_long[Drying_long$Sample == i, ]
colnames(data2model) <- c("Time", "Replicate", "BRC", "Rock", "RWC", "Sample")
(mod <- lme(RWC ~ poly(Time, 2, raw = TRUE), random = ~0 + Time|Replicate, data = data2model))
intervals(mod)
# mod <- lm(`Residual water content (%)` ~ sqrt(1/(`Time (h)` + 1)), data = data2model)
mods[[j]] <- mod
Drying_mods[j, "Sample"] <- i
Drying_mods[j, "Intercept"] <- mod$coefficients$fixed[1]
Drying_mods[j, "b"] <- mod$coefficients$fixed[2]
Drying_mods[j, "a"] <- mod$coefficients$fixed[3]
Drying_mods[j, "P"] <- anova(mod)$`p-value`[2]
Drying_mods[j, "R2"] <- r.squaredGLMM(mod)[, "R2c"]
j <- j + 1
}
Drying_mods %>%
kable(., digits = c(1, 1, 2, 2, 3, 2)) %>%
kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = F)| Sample | Intercept | b | a | P | R2 |
|---|---|---|---|---|---|
| Dolomite Present | 97.2 | -0.85 | 0.01 | 0.002 | 0.86 |
| Dolomite Removed | 91.9 | -2.42 | 0.03 | 0.004 | 0.77 |
| Limestone Present | 97.4 | -0.99 | 0.01 | 0.000 | 0.96 |
| Limestone Removed | 92.5 | -3.83 | 0.05 | 0.000 | 0.92 |
# comapre with and without crust
data2model <- Drying_long
colnames(data2model) <- c("Time", "Replicate", "BRC", "Rock", "RWC", "Sample")
mod_all <- lme(RWC ~ poly(Time, 2, raw = TRUE), random = ~0 + Time|Replicate, data = data2model)
mod_treatment <- lme(RWC ~ poly(Time, 2, raw = TRUE) * BRC, random = ~0 + Time|Replicate, data = data2model)
anova(mod_all, mod_treatment)| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| mod_all | lme.formula(fixed = RWC ~ poly(Time, 2, raw = TRUE), data = data2model, random = ~0 + Time | Replicate) | 1 | 5 | 1108.742 | 1122.884 | -549.3711 | NA | NA | |
| mod_treatment | lme.formula(fixed = RWC ~ poly(Time, 2, raw = TRUE) * BRC, data = data2model, random = ~0 + Time | Replicate) | 2 | 8 | 1024.040 | 1046.472 | -504.0198 | 1 vs 2 | 90.70266 | 0 |
# comapre limestone vs dolomite - with crust
mod_all <- lme(RWC ~ poly(Time, 2, raw = TRUE), random = ~0 + Time|Replicate, data = data2model[data2model$BRC == "Present", ])
mod_treatment <- lme(RWC ~ poly(Time, 2, raw = TRUE) * Rock, random = ~0 + Time|Replicate, data = data2model[data2model$BRC == "Present", ])
anova(mod_all, mod_treatment)| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| mod_all | lme.formula(fixed = RWC ~ poly(Time, 2, raw = TRUE), data = data2model[data2model$BRC == “Present”, ], random = ~0 + Time | Replicate) | 1 | 5 | 409.0114 | 419.5658 | -199.5057 | NA | NA | |
| mod_treatment | lme.formula(fixed = RWC ~ poly(Time, 2, raw = TRUE) * Rock, data = data2model[data2model$BRC == “Present”, ], random = ~0 + Time | Replicate) | 2 | 8 | 409.0676 | 425.5512 | -196.5338 | 1 vs 2 | 5.943792 | 0.1143771 |
# comapre limestone vs dolomite - without crust
mod_all <- lme(RWC ~ poly(Time, 2, raw = TRUE), random = ~0 + Time|Replicate, data = data2model[data2model$BRC == "Removed", ])
mod_treatment <- lme(RWC ~ poly(Time, 2, raw = TRUE) * Rock, random = ~0 + Time|Replicate, data = data2model[data2model$BRC == "Removed", ])
anova(mod_all, mod_treatment)| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| mod_all | lme.formula(fixed = RWC ~ poly(Time, 2, raw = TRUE), data = data2model[data2model$BRC == “Removed”, ], random = ~0 + Time | Replicate) | 1 | 5 | 530.3667 | 540.9211 | -260.1834 | NA | NA | |
| mod_treatment | lme.formula(fixed = RWC ~ poly(Time, 2, raw = TRUE) * Rock, data = data2model[data2model$BRC == “Removed”, ], random = ~0 + Time | Replicate) | 2 | 8 | 525.9703 | 542.4538 | -254.9851 | 1 vs 2 | 10.39642 | 0.0154802 |
# comapre with and without crust - limestone
mod_all <- lme(RWC ~ poly(Time, 2, raw = TRUE), random = ~0 + Time|Replicate, data = data2model[data2model$Rock == "Limestone", ])
mod_treatment <- lme(RWC ~ poly(Time, 2, raw = TRUE) * BRC, random = ~0 + Time|Replicate, data = data2model[data2model$Rock == "Limestone", ])
anova(mod_all, mod_treatment)| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| mod_all | lme.formula(fixed = RWC ~ poly(Time, 2, raw = TRUE), data = data2model[data2model$Rock == “Limestone”, ], random = ~0 + Time | Replicate) | 1 | 5 | 562.9452 | 573.4996 | -276.4726 | NA | NA | |
| mod_treatment | lme.formula(fixed = RWC ~ poly(Time, 2, raw = TRUE) * BRC, data = data2model[data2model$Rock == “Limestone”, ], random = ~0 + Time | Replicate) | 2 | 8 | 497.8492 | 514.3328 | -240.9246 | 1 vs 2 | 71.09599 | 0 |
mod_all <- lme(RWC ~ poly(Time, 2, raw = TRUE), random = ~0 + Time|Replicate, data = data2model[data2model$Rock == "Dolomite", ])
mod_treatment <- lme(RWC ~ poly(Time, 2, raw = TRUE) * BRC, random = ~0 + Time|Replicate, data = data2model[data2model$Rock == "Dolomite", ])
anova(mod_all, mod_treatment)| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| mod_all | lme.formula(fixed = RWC ~ poly(Time, 2, raw = TRUE), data = data2model[data2model$Rock == “Dolomite”, ], random = ~0 + Time | Replicate) | 1 | 5 | 555.4483 | 566.0027 | -272.7241 | NA | NA | |
| mod_treatment | lme.formula(fixed = RWC ~ poly(Time, 2, raw = TRUE) * BRC, data = data2model[data2model$Rock == “Dolomite”, ], random = ~0 + Time | Replicate) | 2 | 8 | 529.4140 | 545.8975 | -256.7070 | 1 vs 2 | 32.03428 | 5e-07 |
p_drying <-
ggplot(
Drying_long,
aes(
x = `Time (h)`,
y = `Residual water content (%)`,
colour = Rock,
# fill = Rock,
shape = BRC,
linetype = BRC
)
) +
geom_point(size = 2, alpha = 2/3) +
# geom_smooth(method = "lm", se = FALSE, alpha = 1/2, formula = (y ~ sqrt(1/(x+1)))) +
geom_smooth(method = "lm", se = TRUE, alpha = 1/3, formula = (y ~ poly(x, 2)), size = 1) +
# geom_line(alpha = 1/2) +
scale_y_continuous(limits = c(0, 100), expand = c(0.01, 0.01)) +
scale_x_continuous(limits = c(0, 50), expand = c(0.01, 0.01)) +
# scale_fill_manual(values = pom4) +
scale_color_manual(values = pom4)
print(p_drying)
devtools::session_info()## ─ Session info ─────────────────────────────────────────────────────────────────────────
## setting value
## version R version 3.4.4 (2018-03-15)
## os KDE neon User Edition 5.14
## system x86_64, linux-gnu
## ui X11
## language en_GB
## collate en_DK.UTF-8
## ctype en_DK.UTF-8
## tz Europe/Vienna
## date 2019-01-04
##
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##
## [1] /home/angel/R/x86_64-pc-linux-gnu-library/3.5
## [2] /usr/local/lib/R/site-library
## [3] /usr/lib/R/site-library
## [4] /usr/lib/R/libraryReferences
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